Method, system and device for image segmentation

Abstract

To provide a method, a system and a device for image segmentation.SOLUTION: A method, a system and a device are described for interacting with an image, segmenting the image, and determining one or more regions of interest within the image.SELECTED DRAWING: Figure 12

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Application No. 62 / 955,644, filed December 31, 2019, which is incorporated herein by reference in its entirety. [Background technology]

[0002] Tumor treating fields, or TTFields, are low-intensity (e.g., 1–3 V / cm) alternating electric fields within the mid-frequency range (100–300 kHz). This noninvasive treatment targets solid tumors and is described in U.S. Patent No. 7,565,205, the entire contents of which are incorporated herein by reference. TTFields disrupt cell division through physical interactions with key molecules during mitosis. TTFields therapy is an approved monotherapy for recurrent glioblastoma and is approved as a combination therapy with chemotherapy for newly diagnosed patients. These fields are induced noninvasively by a transducer array (i.e., an array of electrodes) placed directly on the patient's scalp. TTFields also appears to be beneficial for treating tumors in other parts of the body. Image segmentation tools can be used to optimize TTFields treatment plans. Image segmentation tools require interaction with multiple screens and / or interfaces, are unable to effectively constrain the segmentation to specific regions or boundaries within the image, and require complex actions / procedures to assign structures within the image to distinct structures. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] U.S. Patent No. 7,565,205 [Patent Document 2] U.S. Patent Publication No. 20190117956 A1 [Non-patent literature]

[0004] [Non-Patent Document 1] Ballo et al., "Correlation of Tumor Treating Fields Dosimetry to Survival Outcomes in Newly Diagnosed Glioblastoma: A Large-Scale Numerical Simulation-Based Analysis of Data from the Phase 3 EF-14 Randomized Trial" (2019) Summary of the Invention [Means for solving the problem]

[0005] A method is described that includes the steps of: determining a three-dimensional (3D) model, the 3D model comprising a plurality of voxels; receiving an indication of a selection of an active label, where selection of the active label enables a user indicator to associate voxels of the plurality of voxels with which the user interacts with the active label; receiving an indication of a selection of a paint-over label, where selection of the paint-over label write-protects voxels of the plurality of voxels not associated with the paint-over label from being associated with the active label; receiving an indication of a selection of a restriction label that specifies a structure within the 3D model, where selection of the restriction label write-protects voxels of the plurality of voxels not associated with the structure within the 3D model from being associated with the active label; and associating one or more voxels of the plurality of voxels associated with the paint-over label and the restriction label with the active label based on interaction with the one or more voxels via the user indicator.

[0006] The method also includes the steps of: (a) determining a three-dimensional (3D) model, the 3D model comprising a plurality of voxels, each voxel of the plurality of voxels associated with an illumination intensity value, and each voxel of the plurality of voxels associated with a represented foreground of the 3D model or a represented background of the 3D model based on the respective illumination intensity value; (b) causing display of an interactive element; (c) receiving an indication of a selection of a seed voxel via the interactive element, the seed voxel being associated with an illumination intensity of a particular value; (d) determining one or more voxels of the plurality of voxels having an illumination intensity value within a threshold range of a particular value, the one or more voxels of the plurality of voxels having an illumination intensity value within the threshold range of a particular value being associated with a region of interest (ROI) within the 3D structure; and (e) detecting a change in the illumination intensity value of the one or more voxels associated with the ROI and detecting the change in the illumination intensity value of the one or more voxels associated with the ROI. a change in the illumination intensity value of one or more voxels of the plurality of voxels; (f) causing a change in the represented shape of the ROI and a change in the position of the ROI within the 3D model based on one or more of the changes in the illumination intensity value of one or more voxels associated with the ROI and the changes in the illumination intensity value of one or more voxels of the plurality of voxels; (g) repeating one or more of steps (c)-(f) based on an interaction with an interactive element via a user indicator; and (h) associating one or more voxels of the plurality of voxels with a boundary within the ROI based on another interaction with the interactive element via a user indicator, wherein the illumination intensity values of the one or more voxels associated with the boundary match a specific value; and (i) matching the illumination intensity values of one or more voxels within the boundary to a specific value.

[0007] Also described is a method comprising the steps of determining a three-dimensional (3D) model, the 3D model comprising a plurality of voxels, each voxel of the plurality of voxels being associated with coordinates in the 3D model; determining a structure within the 3D model, the structure comprising one or more voxels of the plurality of voxels; determining another structure within the 3D model, the another structure comprising another one or more voxels of the plurality of voxels; receiving an indication of a selection of the another one or more voxels; receiving a request to modify coordinates of the another one or more voxels based on the indication of the selection of the another one or more voxels; and modifying the coordinates of the another one or more voxels based on the request, wherein modifying the coordinates associates the another structure with the structure.

[0008] Additional advantages will be set forth in part in the description which follows or may be learned by practice. The advantages will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive.

[0009] To easily identify the description of a particular element or act, the most significant digit in a reference number refers to the digit in which that element is first introduced. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 illustrates an exemplary device for electrical therapy. [Figure 2] FIG. 1 illustrates an exemplary transducer array. [Figure 3A] 1A-1C illustrate exemplary applications of the device for electrical therapy. [Figure 3B] 1A-1C illustrate exemplary applications of the device for electrical therapy. [Figure 4A]FIG. 1 illustrates a transducer array placed on a patient's head. [Figure 4B] FIG. 1 illustrates a transducer array placed on the abdomen of a patient. [Figure 5A] FIG. 1 illustrates a transducer array positioned on a patient's torso. [Figure 5B] FIG. 1 illustrates a transducer array placed on the patient's pelvis. [Figure 6] FIG. 1 is a block diagram illustrating an electric field generator and a patient support system. [Figure 7] FIG. 10 shows the electric field magnitude and distribution (in V / cm) shown in the coronal view from a finite element method simulation model. [Figure 8A] FIG. 8 shows a three-dimensional array layout map 800. [Figure 8B] FIG. 1 illustrates the placement of a transducer array on a patient's scalp. [Figure 9A] FIG. 1 shows an axial T1 sequence slice with the most apical image, including the trajectory used to measure head size. [Figure 9B] FIG. 1 shows a coronal T1 sequence slice selecting an image at the level of the ear canal used to measure head size. [Figure 9C] Post-contrast T1 axial images show maximum enhancing tumor diameter used to measure tumor location. [Figure 9D] Post-contrast T1 coronal images show maximum enhancing tumor diameter used to measure tumor location. [Figure 10A] FIG. 10 illustrates an example of a user interface for segmenting an image. [Figure 10B] FIG. 10 illustrates an example of a user interface for segmenting an image. [Figure 10C] FIG. 10 illustrates an example of a user interface for segmenting an image. [Figure 11] FIG. 1 is a block diagram illustrating an exemplary operating environment. [Figure 12] FIG. 1 illustrates an exemplary method. [Figure 13] FIG. 1 illustrates an exemplary method. [Figure 14] FIG. 1 illustrates an exemplary method. DETAILED DESCRIPTION OF THE INVENTION

[0011] Before the present methods and systems are disclosed and described, it is to be understood that the methods and systems are not limited to particular methods, components, or implementations. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

[0012] As used in the specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Ranges may be expressed herein as from "about" one particular value and / or to "about" another particular value. When such a range is expressed, another embodiment includes from the one particular value and / or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent "about," it will be understood that the particular value forms another embodiment. Further, it will be understood that each endpoint of a range is significant both in relation to the other endpoint, and independently of the other endpoint.

[0013] "Optional" or "optionally" means that the subsequently described event or circumstance may or may not occur, and the description includes both the occurrence and non-occurrence of said event or circumstance.

[0014] Throughout the description and claims of this specification, the word "comprise" and variations of the word, such as "comprising" and "comprises," mean "including but not limited to" and are not intended to exclude, for example, other components, integers, or steps. "Exemplary" means "an example of" and is not intended to convey an indication of a preferred or ideal embodiment. "Such as" is used for descriptive purposes, not in a limiting sense.

[0015] Disclosed are components that can be used to implement the disclosed methods and systems. These and other components are disclosed herein, and where combinations, subsets, interactions, groups, etc. of these components are disclosed, specific reference to each of the various individual and collective combinations and permutations thereof may not be explicitly disclosed, but each is specifically contemplated and described herein for all methods and systems. This applies to all aspects of this application, including, but not limited to, steps in the disclosed methods. Thus, where there are various additional steps that can be performed, it is understood that each of these additional steps can be performed in any specific embodiment or combination of embodiments of the disclosed methods.

[0016] The method and system of the present invention may be more readily understood by reference to the following detailed description of the preferred embodiments and examples contained therein, as well as the drawings and their accompanying description.

[0017] As will be appreciated by those skilled in the art, the methods and systems may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the methods and systems may take the form of a computer program product on a computer-readable storage medium having computer-readable program instructions (e.g., computer software) embodied in the storage medium. More specifically, the methods and systems of the present invention may take the form of web-implemented computer software. Any suitable computer-readable storage medium may be utilized, including a hard disk, a CD-ROM, an optical storage device, or a magnetic storage device.

[0018] Embodiments of the methods and systems are described below with reference to block diagrams and flowchart illustrations of methods, systems, apparatuses, and computer program products. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by computer program instructions. These computer program instructions can be loaded into a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, executing on the computer or other programmable data processing apparatus, create means for implementing the function specified in the flowchart block or blocks.

[0019] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including computer-readable instructions for implementing the functions specified in the flowchart block or blocks. The computer program instructions may also be loaded into a computer or other programmable data processing apparatus to cause the computer or other programmable apparatus to perform a series of operational steps to generate a computer-implemented process, such that the instructions executing on the computer or other programmable apparatus provide the steps for implementing the functions specified in the flowchart block or blocks.

[0020] Thus, the blocks in the block diagrams and flowchart diagrams support combinations of means for performing a specified function, combinations of steps for performing a specified function, and combinations of program instruction means for performing a specified function. It will also be understood that each block of the block diagrams and flowchart diagrams, and combinations of blocks in the block diagrams and flowchart diagrams, can be implemented by a dedicated hardware-based computer system that performs the specified function or step, or a combination of dedicated hardware and computer instructions.

[0021] TTFields, also referred to herein as alternating current electric fields, have been established as an anti-mitotic cancer treatment because they disrupt proper microtubule assembly during metaphase, ultimately disrupting cells during telophase and cytokinesis. Efficacy increases with increasing field strength, and the optimal frequency depends on the cancer cell line, with 200 kHz being the frequency at which TTFields induces the highest inhibition of glioma cell growth. For cancer treatment, for example, for patients with glioblastoma multiforme (GBM), the most common primary malignant brain tumor in humans, non-invasive devices with capacitively coupled transducers placed directly on the skin near the tumor have been developed.

[0022] Because the effects of TTFields are directional—cells dividing parallel to the field are more affected than those dividing in other directions—and because cells divide in all directions, TTFields are typically delivered through two pairs of transducer arrays that generate a vertical field within the treated tumor. More specifically, one pair of transducer arrays may be positioned on the left and right (LR) sides of the tumor, and the other pair of transducer arrays may be positioned on the anterior and posterior (AP) sides of the tumor. Cycling the field between these two directions (i.e., LR and AP) ensures that the widest range of cell directions is targeted. Other positions of the transducer arrays are contemplated beyond the vertical field. In one embodiment, asymmetric positioning of three transducer arrays is contemplated, where one pair of the three transducer arrays may deliver an AC field, then another pair of the three transducer arrays may deliver an AC field, then the remaining pair of the three transducer arrays may deliver an AC field.

[0023] In vivo and in vitro studies have shown that the effectiveness of TTFields therapy increases as the strength of the electric field increases. Therefore, optimizing the placement of the array on the patient's scalp to enhance intensity in affected areas of the brain is standard practice for the Optune system. Optimizing array placement can be performed by "rules of thumb" (e.g., placing the array on the scalp as close to the tumor as possible), measurements representing the patient's head shape, tumor dimensions, and / or tumor location. Measurements used as inputs can be derived from imaging data. Imaging data is intended to include any type of visual data, such as single-photon emission computed tomography (SPECT) image data, X-ray computed tomography (X-ray CT) data, magnetic resonance imaging (MRI) data, positron emission tomography (PET) data, or data that can be captured by optical instruments (e.g., photographic cameras, charge-coupled device (CCD) cameras, infrared cameras, etc.). In certain implementations, the image data can include 3D data (e.g., point cloud data) obtained from or generated by a 3D scanner. Optimization relies on understanding how the electric field is distributed within the head as a function of array position, and in some embodiments can take into account variations in the distribution of electrical properties within the heads of different patients.

[0024] FIG. 1 illustrates an exemplary apparatus 100 for electrical therapy. Generally, apparatus 100 may be a portable, battery- or mains-powered device that generates alternating electric fields within the body through a non-invasive surface transducer array. Apparatus 100 may include an electric field generator 102 and one or more transducer arrays 104. Apparatus 100 may be configured to generate tumor treating fields (TTFields) (e.g., at 150 kHz) via electric field generator 102 and deliver the TTFields to a region of the body through one or more transducer arrays 104. Field generator 102 may be a battery- and / or mains-powered device. In one embodiment, one or more transducer arrays 104 are uniformly shaped. In one embodiment, one or more transducer arrays 104 are not uniformly shaped.

[0025] The electric field generator 102 may include a processor 106 in communication with a signal generator 108. The electric field generator 102 may include control software 110 configured to control the performance of the processor 106 and the signal generator 108.

[0026] The signal generator 108 may generate one or more electrical signals in the form of a waveform or pulse train. The signal generator 108 may be configured to generate an alternating voltage waveform (e.g., TTFields) at a frequency ranging from about 50 kHz to about 500 kHz (preferably, from about 100 kHz to about 300 kHz). The voltage is such that the electric field strength in the treated tissue ranges from about 0.1 V / cm to about 10 V / cm.

[0027] One or more outputs 114 of the electric field generator 102 may be coupled at one end to one or more conductive leads 112 attached to the signal generator 108. Both ends of the conductive leads 112 are connected to one or more transducer arrays 104 that are activated by an electrical signal (e.g., a waveform). The conductive leads 112 may comprise standard insulated conductors with flexible metal shielding and may be grounded to prevent the electric field generated by the conductive leads 112 from spreading. The one or more outputs 114 may be operated sequentially. Output parameters of the signal generator 108 may include, for example, field strength, wave frequency (e.g., treatment frequency), and maximum allowable temperature of the one or more transducer arrays 104. The output parameters may be set and / or determined by control software 110 in conjunction with the processor 106. After determining the desired (e.g., optimal) treatment frequency, the control software 110 may cause the processor 106 to send a control signal to the signal generator 108, causing the signal generator 108 to output the desired treatment frequency to one or more transducer arrays 104.

[0028] The one or more transducer arrays 104 may be configured in various shapes and positions to generate electric fields of desired configurations, directions, and strengths at the target volume to focus the treatment. The one or more transducer arrays 104 may be configured to deliver two perpendicular field directions through the volume of interest.

[0029] The one or more transducer arrays 104 may include one or more electrodes 116. The one or more electrodes 116 may be made of any material having a high dielectric constant. The one or more electrodes 116 may include, for example, one or more insulated ceramic disks. The electrodes 116 may be biocompatible and coupled to a flexible circuit board 118. The electrodes 116 may be configured to not make direct contact with the skin (similar to those found in electrocardiogram pads) because the electrodes 116 are separated from the skin by a layer of conductive hydrogel (not shown).

[0030] The electrodes 116, hydrogel, and flexible circuit board 118 may be attached to a hypoallergenic medical bandage 120 to maintain the one or more transducer arrays 104 in place on the body and in continuous direct contact with the skin. Each transducer array 104 may include one or more thermistors (not shown), for example, eight thermistors (with an accuracy of ±1°C) for measuring skin temperature beneath the transducer array 104. The thermistors may be configured to measure skin temperature periodically, for example, every second. The thermistors may be read by the control software 110 when TTFields are not being delivered to avoid interference with temperature measurements.

[0031] If the measured temperature falls below a preset maximum temperature (Tmax), e.g., 38.5°C to 40.0°C ± 0.3°C, between two subsequent measurements, the control software 110 can increase the current until it reaches the maximum treatment current (e.g., 4 amps peak-to-peak). If the temperature reaches Tmax + 0.3°C and continues to rise, the control software 110 can decrease the current. If the temperature rises to 41°C, the control software 110 can shut off TTFields therapy and trigger an overheating alarm.

[0032] The one or more transducer arrays 104 may vary in size and may include various numbers of electrodes 116 based on the size of the patient's body and / or different treatments. For example, in the context of a patient's chest, the smaller transducer arrays may include 13 electrodes each and the larger transducer arrays may include 20 electrodes each, with the electrodes interconnected in series in each array. For example, in the context of a patient's head, as shown in FIG. 2, the transducer arrays may include 9 electrodes each and the electrodes interconnected in series in each array.

[0033] Alternative structures for the transducer array(s) 104 are contemplated and may be used, including, for example, transducer arrays that use ceramic elements that are not disk-shaped and transducer arrays that use non-ceramic dielectric materials disposed on multiple flat conductors. Examples of the latter include polymer films disposed on pads on a printed circuit board or on flat metal strips. Transducer arrays that use electrode elements that are not capacitively coupled may also be used. In this situation, each element of the transducer array is implemented using an area of conductive material configured for placement against the subject / patient's body, with no insulating dielectric layer disposed between the conductive element and the body. Other alternative structures for implementing the transducer array may also be used. Any transducer array (or similar device / component) configuration, arrangement, type, etc. may be used for the methods and systems described herein, so long as the transducer array (or similar device / component) configuration, arrangement, type, etc. (a) is capable of delivering TTFields to the subject / patient's body and (b) can be positioned and / or disposed on a portion of the patient / subject's body as described herein.

[0034] The status and monitored parameters of device 100 may be stored in memory (not shown) and transferred to a computing device via a wired or wireless connection. Device 100 may include a display (not shown) for displaying visual indicators such as power on, therapy on, alarms, and low battery.

[0035] 3A and 3B illustrate an exemplary application of the device 100. Transducer arrays 104a and 104b are shown, each incorporated into a hypoallergenic medical bandage 120a and 120b, respectively. The hypoallergenic medical bandages 120a and 120b are applied to a skin surface 302. A tumor 304 is located beneath the skin surface 302 and bone tissue 306, and within brain tissue 308. The electric field generator 102 causes the transducer arrays 104a and 104b to generate an alternating electric field 310 within the brain tissue 308, which disrupts the rapid cell division exhibited by cancer cells in the tumor 304. The alternating electric field 310 has been shown in non-clinical experiments to inhibit tumor cell growth and / or destroy them. The use of the alternating electric field 310 takes advantage of the special properties, geometry, and division rate of cancer cells, making them susceptible to the alternating electric field 310. The AC electric field 310 changes its polarity at intermediate frequencies (approximately 100-300 kHz). The frequency used for a particular treatment may be specific to the cell type being treated (e.g., 150 kHz for MPM). AC electric fields 310 have been shown to disrupt mitotic spindle microtubule assembly and induce dielectrophoretic rearrangement of intracellular macromolecules and organelles during cytokinesis. These processes lead to physical disruption of the cell membrane and programmed cell death (apoptosis).

[0036] Because the effect of the AC electric field 310 is directional, with cells dividing parallel to the field being more affected than cells dividing in other directions, and because cells divide in all directions, the AC electric field 310 can be delivered through two pairs of transducer arrays 104 to generate a perpendicular field within the treated tumor. More specifically, one pair of transducer arrays 104 can be positioned on the left and right (LR) sides of the tumor, and the other pair of transducer arrays 104 can be positioned on the anterior and posterior (AP) sides of the tumor. Cycling the AC electric field 310 between these two directions (e.g., LR and AP) ensures that the greatest range of cell directions is targeted. In one embodiment, the AC electric field 310 can be delivered according to a symmetrical setup of the transducer arrays 104 (e.g., four total transducer arrays 104, two matched pairs). In another embodiment, the AC electric field 310 can be delivered according to an asymmetrical setup of the transducer arrays 104 (e.g., three total transducer arrays 104). An asymmetric setup of the transducer arrays 104 may involve engaging two of the three transducer arrays 104 to deliver the alternating electric field 310, then switching to another two of the three transducer arrays 104 to deliver the alternating electric field 310, and so on.

[0037] In vivo and in vitro studies have shown that the effectiveness of TTFields therapy increases as the strength of the electric field increases. The described methods, systems, and devices are configured to optimize the placement of the array on the patient's scalp to enhance intensity in affected areas of the brain.

[0038] As shown in Figure 4A, the transducer array 104 may be placed on the patient's head. As shown in Figure 4B, the transducer array 104 may be placed on the patient's abdomen. As shown in Figure 5A, the transducer array 104 may be placed on the patient's torso. As shown in Figure 5B, the transducer array 104 may be placed on the patient's pelvis. It is specifically contemplated that the transducer array 104 may be placed on other parts of the patient's body (e.g., arms, legs, etc.).

[0039] 6 is a block diagram illustrating a non-limiting example of a system 600 including a patient assistance system 602. The patient assistance system 602 may include one or more computers configured to run and / or store an electric field generator (EFG) configuration application 606, a patient modeling application 608, and / or imaging data 610. The patient assistance system 602 may include, for example, a computing device. The patient assistance system 602 may include, for example, a laptop computer, a desktop computer, a mobile phone (e.g., a smartphone), a tablet, etc.

[0040] The patient modeling application 608 may be configured to generate a three-dimensional model (e.g., a patient model) of a portion of a patient's body according to imaging data 610. The imaging data 610 may comprise any type of visual data, such as, for example, single-photon emission computed tomography (SPECT) image data, X-ray computed tomography (X-ray CT) data, magnetic resonance imaging (MRI) data, positron emission tomography (PET) data, data that can be captured by an optical instrument (e.g., a photographic camera, a charge-coupled device (CCD) camera, an infrared camera, etc.). In certain implementations, the image data may include 3D data (e.g., point cloud data) obtained from or generated by a 3D scanner. The patient modeling application 608 may also be configured to generate a three-dimensional array layout map based on the patient model and one or more electric field simulations.

[0041] To appropriately optimize array placement on a patient's body part, imaging data 610, such as MRI imaging data, can be analyzed by a patient modeling application 608 to identify regions of interest comprising tumors. In the context of a patient's head, a modeling framework based on an anatomical head model using finite element method (FEM) simulations can be used to characterize how electric fields behave and distribute within the human head. These simulations generate realistic head models based on magnetic resonance imaging (MRI) measurements and compartmentalize tissue types within the head, such as the skull, white matter, gray matter, and cerebrospinal fluid (CSF). Each tissue type can be assigned dielectric properties of relative conductivity and permittivity, and various transducer array configurations can be applied to the surface of the model to run simulations to understand how an externally applied electric field of a preset frequency distributes through any part of the patient's body, such as the brain. Results of these simulations, using a paired array configuration, a constant current, and a preset frequency of 200 kHz, demonstrated that the electric field distribution is relatively nonuniform throughout the brain, with field strengths exceeding 1 V / cm being generated in most tissue compartments except for the CSF. These results were obtained assuming a peak-to-peak total current of 1800 milliamperes (mA) at the transducer array-scalp interface. This threshold of electric field strength is sufficient to inhibit cell proliferation of glioblastoma cell lines. Furthermore, by manipulating the paired transducer array configuration, it is possible to achieve nearly three times the electric field strength to specific brain regions, as shown in Figure 7. Figure 7 shows the electric field magnitude and distribution (in V / cm) shown in a coronal view from a finite element method simulation model. This simulation employs a left-right paired transducer array configuration.

[0042] In one embodiment, the patient modeling application 608 can be configured to determine a desired (e.g., optimal) transducer array layout for a patient based on the location and extent of the tumor. For example, initial morphometric head size measurements can be determined from a T1 sequence of a brain MRI using axial and coronal views. Post-contrast axial and coronal MRI slices can be selected to show the maximum diameter of lesion enhancement. Various permutations and pairwise array layout combinations can be evaluated using the head size measurements and the distance from predetermined fiducial markers to the tumor margin to generate a configuration that delivers the maximum field strength to the tumor site. The output can be a three-dimensional array layout map 800, as shown in FIG. 8A. The three-dimensional array layout map 800 can be used by the patient and / or caregiver in positioning the array on the scalp during the normal course of TTFields treatment, as shown in FIG. 8B.

[0043] In one aspect, the patient modeling application 608 can be configured to determine a three-dimensional array layout map of the patient. MRI measurements of a portion of the patient to receive the transducer array can be determined. By way of example, the MRI measurements can be received via a standard Digital Imaging and Communications in Medicine (DICOM) viewer. The determination of the MRI measurements can be performed automatically, for example, by artificial intelligence techniques, or manually, for example, by a physician.

[0044] Manual MRI measurement determination may comprise receiving and / or providing MRI data via a DICOM viewer. The MRI data may comprise a scan of a portion of a patient including a tumor. By way of example, in the context of a patient's head, the MRI data may comprise a head scan including one or more of a right frontotemporal tumor, a right parietotemporal tumor, a left frontotemporal tumor, a left parieto-occipital tumor, and / or a multifocal midline tumor. Figures 9A, 9B, 9C, and 9D show example MRI data illustrating a scan of a patient's head. Figure 9A shows an axial T1 sequence slice including the apical-most image, including the trajectory used to measure head size. Figure 9B shows a coronal T1 sequence slice selecting an image at the level of the ear canal, used to measure head size. Figure 9C shows a post-contrast T1 axial image showing the maximum enhancing tumor diameter used to measure tumor location. Figure 9D shows a post-contrast T1 coronal image showing the maximum enhancing tumor diameter used to measure tumor location. MRI measurements may begin from fiducial markers at the outer edge of the scalp and extend tangentially to the right, front, and top. Morphometric head size may be estimated from an axial T1 MRI sequence selecting the most apical image that still includes the orbits (or the image directly above the top edge of the orbits).

[0045] In one embodiment, the MRI measurements may comprise, for example, one or more of head size measurements and / or tumor measurements. In one embodiment, the one or more MRI measurements may be rounded to the nearest millimeter and provided to a transducer array placement module (e.g., software) for analysis. The MRI measurements may then be used to generate a three-dimensional array layout map (e.g., three-dimensional array layout map 800).

[0046] The MRI measurements may comprise one or more head size measurements, such as maximum anterior-posterior (A-P) head size starting from the outer edge of the scalp, maximum head width perpendicular to the A-P measurement, lateral distance from right to left, and / or distance from the right edge of the scalp to the anatomical midline.

[0047] The MRI measurements may include one or more head size measurements, such as a coronal head size measurement. The coronal head size measurement may be obtained on a T1 MRI sequence that selects an image at the level of the ear canal (FIG. 9B). The coronal head size measurement may include one or more of a vertical measurement from the vertex of the scalp to an orthogonal line depicting the inferior border of the temporal lobe, a maximum lateral head width from right to left, and / or a distance from the right edge of the scalp to the anatomical midline.

[0048] The MRI measurements may include one or more tumor measurements, such as tumor location measurements. Tumor location measurements may be performed first on axial images showing the largest enhancing tumor diameter using a T1 post-contrast MRI sequence ( FIG. 9C ). The tumor location measurements may include one or more of: maximum A-P head size excluding the nose; maximum right-to-left lateral diameter measured perpendicular to the A-P distance; distance from the right edge of the scalp to the anatomical midline; distance from the right edge of the scalp to the nearest tumor edge measured parallel to the left-right lateral distance and perpendicular to the A-P measurement; distance from the right edge of the scalp to the farthest tumor edge measured parallel to the left-right lateral distance and perpendicular to the A-P measurement; distance from the front of the head to the nearest tumor edge measured parallel to the A-P measurement; and / or distance from the front of the head to the farthest tumor edge measured parallel to the A-P measurement.

[0049] The one or more tumor measurements may include a coronal tumor measurement. A coronal tumor measurement may comprise identifying a post-contrast T1 MRI slice characterized by the maximum diameter of tumor enhancement (FIG. 9D). A coronal tumor measurement may comprise one or more of the maximum distance from the vertex of the scalp to the inferior border of the cerebrum. In anterior slices, this is delimited by a horizontal line drawn to the inferior border of the frontal or temporal lobe, and in posterior slices, by a horizontal line extending to the lowest level of the visible tentorium; the maximum lateral head width from right to left; the distance from the right edge of the scalp to the anatomical midline; the distance from the right edge of the scalp to the nearest tumor edge measured parallel to the left-right lateral distance; the distance from the right edge of the scalp to the farthest tumor edge measured parallel to the left-right lateral distance; the distance from the parietal to the nearest tumor edge measured parallel to the superior cerebral line; and / or the distance from the parietal to the farthest tumor edge measured parallel to the superior cerebral line.

[0050] Other MRI measurements may be used, particularly if the tumor is located in another part of the patient's body. The MRI measurements may be used by the patient modeling application 608 to generate a patient model. In some cases, measurements may be derived from images other than MRI images, such as images (e.g., image data 610) derived from radiography, ultrasound, elastography, photoacoustic imaging, positron emission tomography, echocardiography, magnetic particle imaging, functional near-infrared spectroscopy, etc. Any image may be used to generate the patient model. The patient model may be used to optimize the TTFields treatment plan. For example, the patient model may have electrical properties assigned to various tissue types identified in the model, and the patient model may be used to determine a 3D array layout map (e.g., 3D array layout map 800). Table 1 shows typical electrical properties of tissues that may be used in the simulation.

[0051] [Table 1]

[0052] Continuing with the example of a tumor in a patient's head, a healthy head model can be generated to serve as a deformable template from which a patient model can be created. To create the patient model, the tumor can be segmented from the patient's MRI data (e.g., one or more MRI measurements). Segmenting the MRI data identifies tissue types at each voxel, and electrical properties can be assigned to each tissue type based on empirical data. The region of the tumor in the patient's MRI data can be masked, and a non-rigid registration algorithm can be used to register the remaining regions of the patient's head to a 3D discrete image representing a deformable template of the healthy head model. This process generates a non-rigid transformation that maps the healthy portion of the patient's head to template space, as well as an inverse transformation that maps the template to patient space. The inverse transformation is applied to the 3D deformable template to generate an approximation of the patient's head in the absence of the tumor. Finally, the tumor (called the region of interest (ROI)) is transferred back to the deformed template to generate a complete patient model. The patient model can be a digital representation in three-dimensional space of a patient's body part, including internal structures such as tissues, organs, and tumors.

[0053] An image derived from the image data 610 (e.g., a three-dimensional (3D) image, etc.) may include multiple voxels representing anatomical structures (e.g., tissues, organs, tumors, etc.) present in the patient from which the model was derived. For example, each voxel in the image may be assigned to three types of structures: a tissue structure, a region of interest (ROI) structure, and an avoidance-type structure. A tissue-type structure may define the tissue type (e.g., gray matter, white matter, skin, skull, enhancing tumor, etc.) and associated electrical properties (e.g., conductivity, permittivity, etc.) of a voxel in the image. A ROI structure may include a collection of voxels in the patient model that are of interest to a user. For example, a user may want to optimize treatment by maximizing power loss associated with electric fields within a particular ROI or may want to calculate the average electric field strength within a particular ROI. An avoidance-region-type structure may include areas in the image (and / or patient model) where transducer array placement should be avoided when simulating TTFields delivery.

[0054] The patient modeling application 608 may include semi-automatic tools / functionality that allow a user to segment images used to create a patient model and assign voxels to various structures. In some cases, the patient modeling application 608 may use overwrite logic and / or tools that allow a user to segment an image. For example, when segmenting an image, a user can assign voxels to specific structures via automatic / semi-automatic tools and / or manual tools such as a paintbrush. A user can assign voxels / limit the area to which voxels are assigned. A user can select / indicate active labels for structures to which voxels are assigned during segmentation. A user can select / indicate paint-over labels for voxels that are to be painted over (e.g., overwritten), such as voxels within structures assigned to a target structure defined by an active label. A user can select / indicate restriction labels to define structures to which the segmentation should be restricted. For example, voxels that can be assigned active labels may be limited to voxels assigned to structures associated with the paint-over label and structures associated with the restriction label during segmentation. For example, if a user is using the patient modeling application 608 to segment a tumor into different tissue types, the user may select "active tumor" (e.g., tumor to be segmented) as the active label. The user may use the patient modeling application 608 to override voxels that are not assigned to a tissue type and may use the patient modeling application 608 to restrict the segmentation to an ROI that defines the volume of the tumor to be segmented. FIG. 10A shows a user interface 1000 of the patient modeling application 608. The user interface 1000 may be an interactive element of the patient modeling application 608, such as a screen, a page, etc.As shown, a user has selected an active label 1010 that assigns voxels of the image to a structure called necrosis. The paint-over label 1020 indicates "any tissue," indicating that any new voxel assignment may overwrite any voxels previously assigned to any type of tissue structure in the image. The restriction label 1030 indicates that the segmentation is restricted to (e.g., may only affect, may overwrite, etc.) voxels of the image that are assigned to a region of interest, such as "ROI 1."

[0055] In some cases, the patient modeling application 608 may include one or more interactive, flood-fill-type algorithms (e.g., region-growing algorithms, level-set algorithms, active contour-based algorithms, etc.) that segment an ROI from an image based on seed voxels (e.g., seed volumes / curves, etc.) within the ROI. As the patient modeling application 608 begins segmentation, the seed voxels may change shape until they capture a connected region in the image (e.g., foreground, etc.) that can be distinguished from its surroundings (e.g., background, etc.) based on characteristics such as grayscale level, patterns / textures within the region, clear edges between the foreground and background, etc. FIG. 10B illustrates a user interface 1000 of the patient modeling application 608. In some cases, the user interface 1000 may be an interactive element of the patient modeling application 608 that allows any / all user actions to be performed via the user interface 1000, such as a single screen, page, etc. In some cases, the user interface 1000 may include multiple screens, pages, etc. Once initiated, the user interface 1000 may allow a user to use interactive tools (e.g., a mouse, cursor, keyboard, etc.) to place seeds (e.g., seed voxels, etc.) in images such as images 1001, 1002, 1003, and 1004. Once the seeds are placed, the ROI 1005 may be displayed in images 1001, 1002, 1003, and 1004, and auto-thresholding may be performed. The patient modeling application 608 may receive one or more commands / signals (e.g., via an interactive tool, etc.) to modify the shape and / or position of the ROI, modify the threshold used to separate foreground and background, remove and / or add additional seeds, etc. In some cases, seeds placed outside the ROI may be ignored. The ROI may be updated based on the location of the seeds.The user interface 1000 enables the patient modeling application 608 to receive commands / signals (e.g., commands to place / adjust seeds, set thresholds, change the shape / position of an ROI, etc.) in an iterative and non-sequential manner. In some cases, interaction with the user interface 1000 can cause the patient modeling application 608 to terminate, stop, and / or pause the initiation / progression of a region-filling algorithm in specific regions of the images 1001, 1002, 1003, and 1004. For example, interactive tools can be used to mark voxels in the images 1001, 1002, 1003, and 1004 to form barriers beyond which the region of segmentation cannot grow. For example, if a user uses the user interface 1000 of the patient modeling application 608 to segment a resection cavity depicted on an image that is located near a cardiac ventricle depicted on the image, voxels can be marked / selected to indicate a blocked region located at the boundary between the resection cavity and the cardiac ventricle. The marked / selected voxels may form boundaries that the region-filling algorithm cannot pass to ensure that the resection cavity is correctly segmented without the region bleeding into the ventricle. In some cases, the user interface 1000 may include one or more advanced options that allow voxels to be marked / selected and / or one or more boundaries to be created in the image.

[0056] In some cases, the user interface 1000 may allow a first structure represented in an image to be assigned to a second structure represented in the image by associating all voxels associated with the first structure with the second structure. For example, as shown in FIG. 10B , at 1006, voxels associated with an ROI indicate / represent that the gross tumor volume (GTV) may be assigned to a tissue type of an enhancing tumor. The assignment of voxels to structures may be based on the patient modeling application 608 receiving one or more commands / signals via any method / means. In some cases, the assignment of voxels to structures may be based on the patient modeling application 608 receiving one or more commands / signals via an interactive tool, such as a click (e.g., a mouse click) and / or a drag command. For example, to assign voxels belonging to a first structure to a second structure, a user can move an interactive tool over the first structure, click / select / mark the first structure via the interactive tool, drag the first structure to the second structure via the interactive tool, and drop the first structure on the second structure via the interactive tool (e.g., to associate coordinates of voxels in the first structure with coordinates of voxels in the second structure, etc.). Any number of structures can be assigned to another structure. Once the image is segmented, an interactive component 1006 of the user interface 1000 can be selected (e.g., interacted with, etc.) to cause the patient modeling application 608 to generate / create a patient model.

[0057] Delivery of TTFields may be simulated by a patient modeling application 608 using a patient model. Simulated field distributions, dosimetry, and simulation-based analyses are described in U.S. Patent Publication No. 20190117956 A1 and in the publication by Ballo et al., "Correlation of Tumor Treating Fields Dosimetry to Survival Outcomes in Newly Diagnosed Glioblastoma: A Large-Scale Numerical Simulation-based Analysis of Data from the Phase 3 EF-14 Randomized Trial" (2019), which are incorporated herein by reference in their entireties.

[0058] To ensure systematic placement of the transducer array relative to the tumor location, a reference coordinate system can be defined. For example, the transverse plane can be first defined by conventional LR and AP positioning of the transducer array. The left-right direction can be defined as the x-axis, the AP direction as the y-axis, and the craniocaudal direction perpendicular to the XY plane as the z-axis.

[0059] After defining the coordinate system, the transducer arrays can be virtually positioned on the patient model using their centers and longitudinal axes in the XY plane. The pair of transducer arrays can be systematically rotated from 0 to 180 degrees around the z-axis of the head model, i.e., in the XY plane, thereby covering the entire circumference of the head (symmetrically). The rotation interval can be, for example, 15 degrees, corresponding to a translation of approximately 2 cm, giving a total of 12 different positions in the 180-degree range. Other rotation intervals are possible. Calculations of the electric field distribution can be performed for each transducer array position relative to the tumor coordinates.

[0060] The electric field distribution in the patient model can be determined by the patient modeling application 608 using a finite element (FE) approximation of the electric potential. Generally, quantities defining time-varying electromagnetic fields are given by complex Maxwell's equations. However, at low to mid-frequency (f = 200 kHz) for biological tissue and TTFields, the electromagnetic wavelength is much larger than the size of the head, and the permittivity ε can be neglected compared to the actual electrical conductivity σ, where ω = 2πf is the angular frequency. This means that electromagnetic propagation effects and capacitive effects in tissue are negligible. Therefore, the scalar electric potential can be adequately approximated by the static Laplace equation ∇(σ∇φ) = 0, using appropriate boundary conditions at the electrodes and skin. Therefore, complex impedances are treated as resistive (i.e., reactance is negligible), and therefore, the current flowing in the volume conductor is primarily free (ohmic) current. The FE approximation of the Laplace equation was calculated using SimNIBS software (simnibs.org). The calculations were based on the Galerkin method, and the conjugate gradient solver residuals had to be less than 1E-9. Dirichlet boundary conditions were used, with the potential set to a fixed (arbitrarily chosen) value at each set of electrode arrays. The electric field (vector field) was calculated as the numerical gradient of the electric potential, and the current density (vector field) was calculated from the electric field using Ohm's law. The electric field value and the potential difference between the current density and the electric field were linearly rescaled to ensure a total peak-to-peak amplitude per array pair of 1.8 A, calculated as the (numerical) surface integral of the normal current density component over all triangular surface elements on the active electrode disk. This corresponds to the current level used for clinical TTFields therapy with the Optune® device. The TTFields "dose" was calculated as the intensity (L2 norm) of the field vector. The modeled current is assumed to be provided by two separate, sequentially active sources, each connected to a pair of 3 × 3 transducer arrays. In the simulation, the left and rear arrays can be defined as sources, while the right and front arrays were corresponding sinks.However, since TTFields uses alternating fields, this choice is arbitrary and does not affect the results.

[0061] The average electric field strength generated by a transducer array placed at multiple locations on a patient may be determined by the patient modeling application 608 for one or more tissue types. In one aspect, the transducer array location corresponding to the highest average electric field strength in the tumor tissue type may be selected as the desired (e.g., optimal) transducer array location for the patient. In another aspect, one or more candidate transducer array locations may be eliminated as a result of the patient's physical condition. For example, one or more candidate locations may be eliminated based on areas of skin inflammation, scarring, surgical sites, discomfort, etc. Thus, after eliminating one or more candidate locations, the transducer array location corresponding to the highest average electric field strength in the tumor tissue type may be selected as the desired (e.g., optimal) transducer array location for the patient. Thus, a transducer array location that results in less than the maximum possible average electric field strength may be selected.

[0062] The patient model may be modified to include indications of desired transducer array locations. The resulting patient model, comprising indications of desired transducer array locations, may be referred to as a three-dimensional array layout map (e.g., three-dimensional array layout map 800). Thus, the three-dimensional array layout map may comprise a digital representation in three-dimensional space of a patient's body part, an indication of a tumor location, indications of locations for placing one or more transducer arrays, combinations thereof, etc.

[0063] The three-dimensional array layout map may be provided to the patient in digital and / or physical form, and the patient and / or patient caregiver may use it to attach one or more transducer arrays to relevant parts of the patient's body (e.g., the head).

[0064] 11 is a block diagram illustrating an environment 1100 comprising a non-limiting example of a patient assistance system 1102. In one aspect, some or all steps of any described method may be performed on a computing device as described herein. The patient assistance system 1102 may comprise one or more computers configured to store one or more of the EFG configuration application 606, the patient modeling application 608, the imaging data 610, etc.

[0065] In terms of hardware architecture, the patient assistance system 1102 may generally be a digital computer including a processor 1109, a memory system 1120, an input / output (I / O) interface 1112, and a network interface 1114. These components (1109, 1120, 1112, and 1114) are communicatively coupled via a local interface 1116. The local interface 1116 may be, for example, but not limited to, one or more buses or other wired or wireless connections as known in the art. The local interface 1116 may have additional elements omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, to enable communication. Additionally, the local interface may include address, control, and / or data connections to enable appropriate communication between the aforementioned components.

[0066] The processor 1109 may be a hardware device for executing software, particularly that stored in the memory system 1120. The processor 1109 may be any custom or commercially available processor, a central processing unit (CPU), a coprocessor among several processors associated with the patient assistance system 1102, a semiconductor-based microprocessor (in the form of a microchip or chipset), or generally any device for executing software instructions. When the patient assistance system 1102 is operating, the processor 1109 may be configured to execute software stored in the memory system 1120, communicate data to and from the memory system 1120, and generally control the operation of the patient assistance system 1102 in accordance with the software.

[0067] The I / O interface 1112 can be used to receive user input from and / or provide system output to one or more devices or components. User input can be provided, for example, via a keyboard and / or a mouse. System output can be provided via a display device and a printer (not shown). The I / O interface 1112 can include, for example, a serial port, a parallel port, a small computer system interface (SCSI), an IR interface, an RF interface, and / or a universal serial bus (USB) interface.

[0068] The network interface 1114 may be used to send and receive data to and from the patient assistance system 1102. The network interface 1114 may include, for example, a 10BaseT Ethernet adapter, a 100BaseT Ethernet adapter, a LAN PHY Ethernet adapter, a token ring adapter, a wireless network adapter (e.g., WiFi), or any other suitable network interface device. The network interface 1114 may include address, control, and / or data connections to enable appropriate communications.

[0069] The memory system 1120 may include any one or more combinations of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)) and non-volatile memory elements (e.g., ROM, hard drives, tape, CD-ROM, DVD-ROM, etc.). Additionally, the memory system 1120 may incorporate electronic, magnetic, optical, and / or other types of storage media. It should be noted that the memory system 1120 may have a distributed architecture in which various components are located remotely from each other, yet may be accessed by the processor 1109.

[0070] The software in the memory system 1120 may include one or more software programs, each of which comprises an ordered list of executable instructions for implementing a logical function. In the example of Figure 11, the software in the memory system 1120 of the patient assistance system 1102 may comprise an EFG configuration application 606, a patient modeling application 608, imaging data 610, and a suitable operating system (O / S) 1118. The operating system 1118 essentially controls the execution of other computer programs and provides scheduling, input / output control, file and data management, memory management, communication control, and related services.

[0071] For purposes of illustration, application programs and other executable program components, such as the operating system 1118, are illustrated herein as separate blocks, with the understanding that such programs and components may reside at various times in different storage components of the patient assistance system 1102. An implementation of the EFG configuration application 606, the patient modeling application 608, the imaging data 610, and / or the control software 1122 may be stored on or transmitted via some form of computer-readable media. Any of the disclosed methods may be performed by computer-readable instructions embodied on a computer-readable medium. A computer-readable medium may be any available medium that can be accessed by a computer. By way of example, and not meant to be limiting, computer-readable media may comprise “computer storage media” and “communications media.” “Computer storage media” may comprise volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. Exemplary computer storage media can comprise RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by a computer.

[0072] In one embodiment, as shown in FIG. 12 , one or more of the apparatus 100, the patient support system 602, the patient modeling application 608, and / or any other devices / components described herein can be configured to perform a method 1200 comprising determining 1210 a three-dimensional (3D) model, wherein the 3D model comprises a plurality of voxels.

[0073] At 1220, an indication of a selection of an active label is received, the selection of the active label enabling a user indicator to associate a voxel of the interacting plurality of voxels with the active label. The user indicator may include one or more of a mouse, a keyboard, a tactile response interface (e.g., a touch screen, etc.), etc.

[0074] At 1230, an indication of a paint over label selection is received, and the selection of the paint over label write protects voxels of the plurality of voxels not associated with the paint over label from being associated with an active label.

[0075] At 1240, an indication of a selection of a restriction label specifying a structure within the 3D model is received, and the selection of the restriction label write-protects voxels of the plurality of voxels not associated with the structure within the 3D model from being associated with the active label.

[0076] At 1250, one or more voxels of the plurality of voxels associated with the paint-over label and the limit label are associated with an active label based on interaction with the one or more voxels via the user indicator.

[0077] In some cases, the method 1200 may include causing the display of the 3D model.

[0078] In some cases, method 1200 may include receiving, via a user indicator, an interaction with one or more other voxels of the plurality of voxels; determining that the one or more other voxels are associated with a paint-over label rather than a restrict label; and ignoring the interaction with the one or more other voxels based on determining that the one or more other voxels are associated with a paint-over label rather than a restrict label.

[0079] In some cases, method 1200 may include receiving, via a user indicator, an interaction with one or more other voxels of the plurality of voxels; determining that the one or more other voxels are not associated with a paint-over label or a restrict label; and ignoring the interaction with the one or more other voxels based on determining that the one or more other voxels are not associated with a paint-over label or a restrict label.

[0080] In some cases, method 1200 may include receiving, via a user indicator, an interaction with one or more other voxels of the plurality of voxels; determining that the one or more other voxels are associated with the restrict label and are not associated with the paint-over label; and ignoring the interaction with the one or more other voxels based on determining that the one or more other voxels are associated with the restrict label and are not associated with the paint-over label.

[0081] 13, one or more of the apparatus 100, the patient support system 602, the patient modeling application 608, and / or any other devices / components described herein may be configured to perform a method 1300 comprising determining 1310 a three-dimensional (3D) model, the 3D model comprising a plurality of voxels, each voxel of the plurality of voxels associated with an illumination intensity value, and each voxel of the plurality of voxels being associated with a represented foreground of the 3D model or a represented background of the 3D model based on the respective illumination intensity value. In some cases, the illumination intensity values may be associated with RGB colors.

[0082] At 1320, the interactive element is caused to be displayed. In some cases, the interactive element may be a single screen, page, etc. that allows all interactive steps of method 1300 to be performed via a single screen, page, etc.

[0083] At 1330, an indication of a selection of a seed voxel is received via an interactive element, the seed voxel being associated with a particular value of illumination intensity.

[0084] At 1340, one or more voxels of the plurality of voxels having illumination intensity values within a threshold range of a particular value are determined, and the one or more voxels of the plurality of voxels having illumination intensity values within the threshold range of the particular value are associated with a region of interest (ROI) within the 3D structure.

[0085] At 1350, causing one or more of a change in illumination intensity values of one or more voxels associated with the ROI and a change in illumination intensity values of one or more voxels of the plurality of voxels.

[0086] At 1360, based on one or more of the change in illumination intensity values of one or more voxels associated with the ROI and the change in illumination intensity values of one or more voxels of the plurality of voxels, cause one or more of a change in the represented shape of the ROI and a change in the position of the ROI within the 3D model.

[0087] At 1370, one or more of steps 1330-1360 are repeated based on interaction with the interactive element via the user indicator.

[0088] At 1380, based on another interaction with the interactive element via the user indicator, one or more voxels of the plurality of voxels are associated with a boundary within the ROI, and the illumination intensity values of the one or more voxels associated with the boundary match a particular value.

[0089] At 1390, the illumination intensity value of one or more voxels within the boundary is matched to a particular value.

[0090] In one embodiment, as shown in FIG. 14 , one or more of the apparatus 100, the patient support system 602, the patient modeling application 608, and / or any other devices / components described herein can be configured to perform a method 1400 comprising, at 1410, determining a three-dimensional (3D) model, the 3D model comprising a plurality of voxels, each voxel of the plurality of voxels being associated with a coordinate within the 3D model.

[0091] At 1420, a structure within the 3D model is determined, the structure comprising one or more voxels of the plurality of voxels.

[0092] At 1430, another structure within the 3D model is determined, the another structure comprising another one or more voxels of the plurality of voxels.

[0093] At 1440, an indication of a selection of another voxel or voxels is received.

[0094] At 1450, a request to modify the coordinates of one or more other voxels based on an indication of the selection of the one or more other voxels is received.

[0095] At 1460, the coordinates of one or more other voxels are modified based on the request, the step of modifying the coordinates associating another structure with the structure.

[0096] In view of the described apparatus, systems, and methods, and variations thereof, certain more particularly described embodiments of the present invention are set forth herein below. However, these specifically recited embodiments should not be construed as having a limiting effect on different claims that incorporate different or more general teachings set forth herein, or as the "particular" embodiments being limited in any way other than in the inherent sense of the language as literally used.

[0097] Embodiment 1: A method comprising: determining a three-dimensional (3D) model, the 3D model comprising a plurality of voxels; receiving an indication of a selection of an active label, wherein selection of the active label enables a user indicator to associate voxels of the plurality of voxels with which the user interacts with the active label; receiving an indication of a selection of a paint-over label, wherein selection of the paint-over label write-protects voxels of the plurality of voxels not associated with the paint-over label from being associated with the active label; receiving an indication of a selection of a restriction label that specifies a structure within the 3D model, wherein selection of the restriction label write-protects voxels of the plurality of voxels not associated with the structure within the 3D model from being associated with the active label; and associating one or more voxels of the plurality of voxels associated with the paint-over label and the restriction label with the active label based on interaction with the one or more voxels via the user indicator.

[0098] Embodiment 2: An embodiment similar to any one of the preceding embodiments, further comprising causing the display of the 3D model.

[0099] Embodiment 3: An embodiment similar to any one of the preceding embodiments, in which the user indicator comprises one or more of a mouse, a keyboard, or a tactile response interface.

[0100] Embodiment 4: An embodiment similar to any one of the preceding embodiments, further comprising: receiving, via a user indicator, an interaction with one or more other voxels of the plurality of voxels; determining that the one or more other voxels are associated with a paint-over label rather than a restrict label; and ignoring the interaction with the one or more other voxels based on determining that the one or more other voxels are associated with a paint-over label rather than a restrict label.

[0101] Embodiment 5: An embodiment similar to any one of embodiments 1 to 3, further comprising the steps of receiving, via a user indicator, an interaction with one or more other voxels of the plurality of voxels, determining that the one or more other voxels are not associated with a paint-over label or a restricted label, and ignoring the interaction with the one or more other voxels based on the step of determining that the one or more other voxels are not associated with a paint-over label or a restricted label.

[0102] Embodiment 6: An embodiment similar to any one of embodiments 1 to 3, further comprising the steps of receiving, via a user indicator, an interaction with one or more other voxels of the plurality of voxels; determining that the one or more other voxels are associated with the restriction label and are not associated with the paint-over label; and ignoring the interaction with the one or more other voxels based on the step of determining that the one or more other voxels are associated with the restriction label and are not associated with the paint-over label.

[0103] Embodiment 7: (a) determining a three-dimensional (3D) model, the 3D model comprising a plurality of voxels, each voxel of the plurality of voxels being associated with an illumination intensity value, and each voxel of the plurality of voxels being associated with a represented foreground of the 3D model or a represented background of the 3D model based on its respective illumination intensity value; (b) causing display of an interactive element; (c) receiving an indication of a selection of a seed voxel via the interactive element, the seed voxel being associated with an illumination intensity of a particular value; (d) determining one or more voxels of the plurality of voxels having an illumination intensity value within a threshold range of a particular value, the one or more voxels of the plurality of voxels having an illumination intensity value within the threshold range of a particular value being associated with a region of interest (ROI) within the 3D structure; and (e) a change in the illumination intensity value of the one or more voxels associated with the ROI. and causing one or more changes in illumination intensity values of one or more voxels of the plurality of voxels; (f) causing one or more changes in the represented shape of the ROI and a change in the position of the ROI within the 3D model based on one or more of the changes in illumination intensity values of one or more voxels associated with the ROI and the changes in illumination intensity values of the one or more voxels of the plurality of voxels; (g) repeating one or more of steps (c)-(f) based on interaction with the interactive element via a user indicator; and (h) associating one or more voxels of the plurality of voxels with a boundary within the ROI based on another interaction with the interactive element via the user indicator, wherein the illumination intensity values of the one or more voxels associated with the boundary match a specific value; and (i) matching the illumination intensity values of one or more voxels within the boundary to a specific value.

[0104] Embodiment 8: An embodiment similar to embodiment 7, in which the lighting intensity values are associated with RGB colors.

[0105] Embodiment 9: A method comprising: determining a three-dimensional (3D) model, the 3D model comprising a plurality of voxels, each voxel of the plurality of voxels being associated with coordinates within the 3D model; determining a structure within the 3D model, the structure comprising one or more voxels of the plurality of voxels; determining another structure within the 3D model, the another structure comprising one or more other voxels of the plurality of voxels; receiving an indication of selection of one or more other voxels; receiving a request to modify coordinates of one or more other voxels based on the indication of selection of the one or more other voxels; and modifying the coordinates of the one or more other voxels based on the request, wherein modifying the coordinates associates the one or more other structures with the structure.

[0106] Unless otherwise expressly stated, it is in no way intended that the methods described herein be construed as requiring that their steps be performed in a particular order. Thus, where a method claim does not actually recite the order in which its steps are to be followed, or where the claims or description do not specifically state that the steps are limited to a particular order, no order is intended to be inferred in any respect. This applies to possible implicit bases of interpretation, including questions of logic regarding the placement or operational flow of steps, the apparent meaning derived from grammatical construction or punctuation, and the number or type of embodiments described herein.

[0107] While the methods and systems have been described in connection with preferred embodiments and specific examples, the embodiments herein are intended in all respects to be illustrative and not restrictive, and therefore are not intended to be limited in scope to the particular embodiments described.

[0108] Unless otherwise expressly stated, it is in no way intended that the methods described herein be construed as requiring that their steps be performed in a particular order. Thus, where a method claim does not actually recite the order in which its steps are to be followed, or where the claims or description do not specifically state that the steps are limited to a particular order, no order is intended to be inferred in any respect. This applies to possible implicit bases of interpretation, including questions of logic regarding the placement or operational flow of steps, the apparent meaning derived from grammatical construction or punctuation, and the number or type of embodiments described herein.

[0109] It will be apparent to those skilled in the art that various modifications and variations can be made without departing from the scope or spirit of the present invention. Other embodiments will be apparent to those skilled in the art from consideration of the specification and practice disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit being indicated by the following claims. [Explanation of symbols]

[0110] 100 devices 102 Electric Field Generator 104 Transducer Array 106 processors 108 Signal Generator 110 Control Software 104a Transducer Array 104b Transducer Array 112 Conductive Lead 114 Output 116 Electrode 118 Flexible circuit board 120a Hypoallergenic Medical Bandage 120b Hypoallergenic Medical Adhesive Bandage 302 Skin surface 304 Tumor 306 Bone tissue 308 Brain Tissue 310 AC Electric Field 600 System 602 Patient Support System 606 Electric Field Generator (EFG) Configuration Applications 608 Patient Modeling Applications 610 Imaging Data 800 3D array layout maps 1000 User Interface 1001 images 1002 images 1003 images 1004 images 1005 ROI 1006 Interactive Components 1010 Active Labels 1020 Paint Over Label 1030 Label 1100 Environment 1102 Patient Support System 1109 processor 1112 Input / Output (I / O) Interface 1114 Network Interface 1116 Local Interface 1118 Operating System (O / S) 1120 Memory System 1122 Control Software 1200 methods

Claims

1. determining a three-dimensional (3D) model, the 3D model comprising a plurality of voxels; receiving an indication of a selection of an active label, the selection of the active label causing a user indicator to associate a voxel of the plurality of voxels with which the user interacts with the active label; receiving an indication of a selection of a paint-over label, wherein said selection of said paint-over label write-protects voxels of said plurality of voxels not associated with said paint-over label from being associated with said active label; receiving an indication of a selection of a restriction label designating a structure within the 3D model, wherein the selection of the restriction label write-protects voxels of the plurality of voxels that are not associated with the structure within the 3D model from being associated with the active label; associating the one or more voxels of the plurality of voxels associated with the paint-over label and the limit label with the active label based on an interaction with one or more voxels via the user indicator; A method comprising:

2. The method of claim 1 , further comprising causing a display of the 3D model.

3. The method of claim 1 , wherein the user indicator comprises one or more of a mouse, a keyboard, or a tactile response interface.

4. receiving, via the user indicator, an interaction with another one or more voxels of the plurality of voxels; determining that the other one or more voxels are associated with the paint-over label rather than the limit label; ignoring the interaction with the one or more other voxels based on determining that the one or more other voxels are associated with the paint-over label rather than the limit label; The method of claim 1 further comprising:

5. receiving, via the user indicator, an interaction with another one or more voxels of the plurality of voxels; determining that the one or more other voxels are not associated with the paint-over label or the limit label; ignoring the interaction with the one or more other voxels based on determining that the one or more other voxels are not associated with the paint-over label or the restrict label; The method of claim 1 further comprising:

6. receiving, via the user indicator, an interaction with another one or more voxels of the plurality of voxels; determining that the other one or more voxels are associated with the restrict label and are not associated with the paint-over label; ignoring the interaction with the one or more other voxels based on determining that the one or more other voxels are associated with the restrict label and are not associated with the paint-over label; and The method of claim 1 further comprising:

7. One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 6.

8. 1. An apparatus comprising: one or more processors; a memory storing processor-executable instructions that, when executed by the one or more processors, cause the apparatus to perform the method of any one of claims 1 to 6; An apparatus comprising:

9. (a) determining a three-dimensional (3D) model, the 3D model comprising a plurality of voxels, each voxel of the plurality of voxels associated with an illumination intensity value, and each voxel of the plurality of voxels associated with a represented foreground of the 3D model or a represented background of the 3D model based on the respective illumination intensity value; (b) causing the display of an interactive element; (c) receiving, via the interactive element, an indication of a selection of a seed voxel, the seed voxel being associated with a particular value of illumination intensity; (d) determining one or more voxels of the plurality of voxels having an illumination intensity value within a threshold range of the particular value, wherein the one or more voxels of the plurality of voxels having the illumination intensity value within the threshold range of the particular value are associated with a region of interest (ROI) within the 3D structure; (e) causing one or more of a change in the illumination intensity values of one or more voxels associated with the ROI and a change in the illumination intensity values of one or more voxels of the plurality of voxels; (f) causing one or more of a change in the represented shape of the ROI and a change in a position of the ROI within the 3D model based on one or more of the change in the illumination intensity values of the one or more voxels associated with the ROI and the change in the illumination intensity values of the one or more voxels of the plurality of voxels; (g) repeating one or more of steps (c) through (f) based on interaction with the interactive element via a user indicator; (h) associating one or more voxels of the plurality of voxels with a boundary within the ROI based on another interaction with the interactive element via the user indicator, wherein the illumination intensity value of the one or more voxels associated with the boundary matches the particular value; (i) matching the illumination intensity values of one or more voxels within the boundary to the specified value; A method comprising:

10. The method of claim 9 , wherein the illumination intensity values are associated with RGB colors.

11. One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by a processor, cause the processor to perform the method of claim 9 or 10.

12. 1. An apparatus comprising: one or more processors; a memory storing processor-executable instructions that, when executed by the one or more processors, cause the apparatus to perform the method of any one of claims 9 or 10; An apparatus comprising:

13. determining a three-dimensional (3D) model, the 3D model comprising a plurality of voxels, each voxel of the plurality of voxels being associated with a coordinate within the 3D model; determining a structure within the 3D model, the structure comprising one or more voxels of the plurality of voxels; determining another structure within the 3D model, the another structure comprising another one or more voxels of the plurality of voxels; receiving an indication of a selection of said one or more additional voxels; receiving a request to modify the coordinates of the one or more other voxels based on the indication of the selection of the one or more other voxels; modifying the coordinates of the one or more other voxels based on the request, wherein modifying the coordinates associates the other structure with the structure; A method comprising:

14. One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by a processor, cause the processor to perform the method of claim 13.

15. 1. An apparatus comprising: one or more processors; a memory storing processor-executable instructions that, when executed by the one or more processors, cause the apparatus to perform the method of claim 13; An apparatus comprising:

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