Methods, systems, and apparatus for image segmentation

Abstract

Methods, systems, and apparatuses for interacting with images, segmenting images, and determining one or more regions of interest within an image are described.

Description

[0001] Cross-referencing of related patent applications

[0002] 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

[0003] Tumor therapeutic fields, or TTFields, are low-intensity (e.g., 1-3 V / cm) alternating electric fields in the mid-frequency range (100-300 kHz). This non-invasive treatment targets solid tumors and is described in U.S. Patent No. 7,565,205, which is incorporated herein by reference in its entirety. TTFields disrupt cell division through physical interactions with key molecules during mitosis. TTFields therapy is approved as a monotherapy for recurrent glioblastoma and as a combination therapy with chemotherapy for newly diagnosed patients. These electric fields are non-invasively sensed by an array of transducers (i.e., an array of electrodes) placed directly on the patient's scalp. TTFields also appear to be advantageous 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, cannot effectively limit segmentation to specific regions or boundaries within an image, and require complex actions / procedures to assign structures within an image to different structures. Summary of the Invention

[0004] A method is described, the method comprising: determining a three-dimensional (3D) model, wherein the 3D model comprises a plurality of voxels; receiving an instruction to select an active label, wherein the selection of the active label enables a user indicator to associate voxels among the plurality of voxels with which it interacts with the active label; receiving an instruction to select a paint-over label, wherein the selection of the paint-over label causes voxels among the plurality of voxels not associated with the paint-over label to be write-protected from being associated with the active label; receiving an instruction to select a limit-to label for a structure within a specified 3D model, wherein the selection of the limit-to label causes voxels among the plurality of voxels not associated with the structure within the 3D model to be write-protected from being associated with the active label; and, based on interaction with one or more voxels via a user indicator, associating one or more voxels among the plurality of voxels associated with the paint-over label and the limit-to label with the active label.

[0005] A method is also described, the method comprising: (a) determining a three-dimensional (3D) model, wherein the 3D model comprises a plurality of voxels, wherein each of the plurality of voxels is associated with an illumination intensity value, wherein each of the plurality of voxels is associated with a foreground or a background of a representation of the 3D model based on the corresponding illumination intensity value; (b) causing an interactive element to be displayed; (c) receiving an instruction for selection of a seed voxel via the interactive element, wherein the seed voxel is associated with an illumination intensity of a specific value; (d) determining that one or more voxels of the plurality of voxels have illumination intensity values ​​within a threshold range of the specific values, wherein the illumination intensity values ​​of one or more voxels of the plurality of voxels within the threshold range of the specific values ​​are associated with a region of interest (ROI) within the 3D structure; and (e) causing an illumination intensity of one or more voxels associated with the ROI. (c) changes in the value and changes in the illumination intensity value of one or more voxels of a plurality of voxels; (f) causing changes in the represented shape of the ROI and changes in the position of the ROI within the 3D model based on changes in the illumination intensity value of one or more voxels associated with the ROI and changes in the illumination intensity value of one or more voxels of a plurality of voxels; (g) repeating one or more of steps (c)-(f) based on interaction with an interactive element via a user indicator; (h) associating one or more voxels of a plurality of voxels with a boundary within the ROI based on another interaction with an interactive element via a user indicator, wherein the illumination intensity value of one or more voxels associated with the boundary matches a specific value; and (i) matching the illumination intensity value of one or more voxels within the boundary with a specific value.

[0006] A method is also described, the method comprising: determining a three-dimensional (3D) model, wherein the 3D model includes a plurality of voxels, wherein each of the plurality of voxels is associated with coordinates within the 3D model; determining a structure within the 3D model, wherein the structure includes one or more voxels among the plurality of voxels; determining another structure within the 3D model, wherein the other structure includes another one or more voxels among the plurality of voxels; receiving an instruction to select the other one or more voxels; based on the instruction to select the other one or more voxels, receiving a request to change the coordinates of the other one or more voxels; and based on the request, changing the coordinates of the other one or more voxels, wherein changing the coordinates associates the other structure with the structure.

[0007] Additional advantages will be set forth in part in the following description or may be learned by practice. These advantages will be realized and obtained through the elements and combinations particularly pointed out in the appended claims. It should be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only, and not restrictive. Attached Figure Description

[0008] For ease of identification of any particular element or action in the discussion, one or more of the most significant digits in the reference numerals refer to the figure number in which the element is first introduced.

[0009] Figure 1 An example device for electrotherapy is shown.

[0010] Figure 2 An example transducer array is shown.

[0011] Figure 3A and Figure 3B The illustration shows an example application of a device used for electrotherapy.

[0012] Figure 4A An array of transducers placed on a patient's head is shown.

[0013] Figure 4B An array of transducers placed on a patient's abdomen is shown.

[0014] Figure 5A It is a transducer array placed on the patient's torso.

[0015] Figure 5B An array of transducers placed on the patient's pelvis is shown.

[0016] Figure 6 This is a block diagram describing the electric field generator and the patient support system.

[0017] Figure 7 The figure shows the electric field amplitude and distribution (in V / cm) as shown in the coronal view from the finite element method simulation model.

[0018] Figure 8A A three-dimensional array layout diagram 800 is shown.

[0019] Figure 8B The placement of the transducer array on the patient's scalp is shown.

[0020] Figure 9A An axial T1 sequence slice containing the topmost image is shown, including orbits used to measure head size.

[0021] Figure 9B Selected images of coronal T1 sequence slices at the level of the ear canal, used to measure head size, are shown.

[0022] Figure 9C An enhanced T1-axial image is shown, which illustrates the maximum enhanced tumor diameter used to measure the tumor location.

[0023] Figure 9DThe enhanced T1 coronal image is shown, which shows the maximum enhanced tumor diameter used to measure the tumor location.

[0024] Figure 10A An example user interface for image segmentation is shown.

[0025] Figure 10B An example user interface for image segmentation is shown.

[0026] Figure 10C An example user interface for image segmentation is shown.

[0027] Figure 11 It is a block diagram depicting the example operating environment.

[0028] Figure 12 An example method is shown.

[0029] Figure 13 An example method is shown.

[0030] Figure 14 An example method is shown. Detailed Implementation

[0031] Before disclosing and describing this method and system, it is to be understood that the method and system are not limited to a particular method, a particular component, or a particular implementation. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0032] As used in the specification and appended claims, the singular forms “a,” “an,” and “the” include a plural of indicators unless the context explicitly specifies otherwise. A range herein may be expressed as from “about” a particular value, and / or to “about” another particular value. When such a range is expressed, another embodiment includes from one particular value and / or to another particular value. Similarly, when a value is expressed as an approximation using the antecedent “about,” it will be understood that the particular value forms another embodiment. It will be further understood that both relation to and independence from the other endpoint are important for each endpoint in the range.

[0033] "Optional" or "optionally" means that the event or situation described below may or may not occur, and the description includes both the scenario in which the event or situation occurs and the scenario in which the event or situation does not occur.

[0034] Throughout the description and claims of this specification, the word "comprising" and variations thereof, such as "including" and "comprising," 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 indications of preferred or ideal embodiments. "Like" is not used in a limiting sense but for interpretive purposes.

[0035] Components that can be used to perform the disclosed methods and systems are disclosed herein. These and other components are disclosed herein, and it is to be understood that while specific references to every different individual and collective combination and arrangement of these components may not be explicitly disclosed, each is specifically considered and described herein for all methods and systems. This applies to all aspects of this application, including but not limited to the steps in the disclosed methods. Therefore, if there are multiple additional steps that can be performed, it is to be understood that each of these additional steps can be performed using any particular embodiment or combination of embodiments of the disclosed method.

[0036] The method and system can be more readily understood by referring to the following detailed description of preferred embodiments and the examples included therein, as well as the accompanying drawings and their preceding and following descriptions.

[0037] As those skilled in the art will understand, the methods and systems may take the form of a completely hardware embodiment, a completely 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) implemented therein. More particularly, the methods and systems may take the form of computer software implemented on the web. Any suitable computer-readable storage medium may be utilized, including hard disks, CD-ROMs, optical storage devices, or magnetic storage devices.

[0038] Embodiments of methods and systems are described below with reference to block diagrams and flowcharts illustrating methods, systems, apparatuses, and computer program products. It will be understood that each block in the block diagrams and flowcharts, as well as combinations of blocks in the block diagrams and flowcharts, can be implemented by computer program instructions. These computer program instructions can be loaded onto a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute on the computer or other programmable data processing apparatus, create instructions for implementing the functions specified in one or more flowchart blocks.

[0039] These computer program instructions may also be stored in a computer-readable storage medium that directs a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture comprising computer-readable instructions for implementing the functions specified in one or more flowchart blocks. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowchart blocks.

[0040] Therefore, the boxes in the block diagrams and flowcharts support combinations of means for performing a specified function, combinations of steps for performing a specified function, and program instruction means for performing a specified function. It will also be understood that each box in the block diagrams and flowcharts, and combinations of boxes in the block diagrams and flowcharts, can be implemented by a dedicated hardware-based computer system or a combination of dedicated hardware and computer instructions that performs the specified function or steps.

[0041] In this paper, TTFields, also known as alternating electric fields, are established as a modality for antimitotic cancer therapy because they interfere with proper microtubule assembly during metaphase and ultimately destroy 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 highest frequency for inhibiting glioma cell growth induced by TTFields. For cancer therapy, non-invasive devices with capacitively coupled transducers have been developed, which are placed directly in the skin region close to the tumor, for example, for patients with glioblastoma multiforme (GBM), the most common primary malignant brain tumor in humans.

[0042] Because TTFields act in a directional manner, cells dividing parallel to the field are more affected than cells dividing in other directions, and because cells divide in all directions, TTFields are typically delivered via two pairs of transducer arrays that generate a vertical field within the treated tumor. More specifically, one pair of transducer arrays may be located on the left and right sides of the tumor (LR), while another pair may be located in front of and behind the tumor (AP). The cyclic field between these two directions (i.e., LR and AP) ensures targeting with the maximum range of cell orientation. Other locations for the transducer arrays besides the vertical field are also considered. In an embodiment, asymmetric positioning of three transducer arrays is considered, where one pair of the three transducer arrays can deliver an alternating electric field, and then another pair of the three transducer arrays can deliver an alternating electric field, and the remaining pair of the three transducer arrays can deliver an alternating electric field.

[0043] In vivo and in vitro studies have shown that the efficacy of TTFields therapy increases with increasing electric field strength. Therefore, optimizing array placement on the patient's scalp to increase intensity in the affected area of ​​the brain is standard practice with the Optune system. Array placement optimization can be performed using "rules of thumb" (e.g., placing the array on the scalp as close to the tumor as possible), measurements describing the geometry of the patient's head, tumor size, and / or tumor location. Measurements used as input 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, data that can be captured by optical instruments (e.g., photographic cameras, charge-coupled device (CCD) cameras, infrared cameras, etc.), and the like. In some implementations, image data may include 3D data (e.g., point cloud data) obtained from or generated by a 3D scanner. Optimization can rely on an understanding of how the electric field is distributed within the head as a function of the array's location, and in some respects, on the variations in the distribution of electrical properties within the head of different patients.

[0044] Figure 1An example device 100 for electrotherapy is shown. Typically, device 100 may be a portable, battery- or power-operated device that generates an alternating electric field within the body via a non-invasive array of surface transducers. Device 100 may include an electric field generator 102 and one or more transducer arrays 104. Device 100 may be configured to generate tumor therapeutic fields (TTFields) (e.g., at 150 kHz) via the electric field generator 102 and deliver the TTFields to areas of the body via one or more transducer arrays 104. The electric field generator 102 may be a battery- and / or power-operated device. In one embodiment, the one or more transducer arrays 104 are uniformly shaped. In another embodiment, the one or more transducer arrays 104 are not uniformly shaped.

[0045] The electric field generator 102 may include a processor 106 that communicates with the 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.

[0046] Signal generator 108 can generate one or more electrical signals in the form of waveforms or pulse sequences. Signal generator 108 can be configured to generate AC voltage waveforms (e.g., TTFields) at frequencies ranging from about 50 kHz to about 500 kHz (preferably from about 100 kHz to about 300 kHz). The voltage causes the electric field strength in the tissue to be treated to be in the range of about 0.1 V / cm to about 10 V / cm.

[0047] One or more outputs 114 of the electric field generator 102 may be coupled to one or more conductive leads 112, one end of which is attached to the signal generator 108. The opposite end of the conductive leads 112 is connected to one or more transducer arrays 104 activated by an electrical signal (e.g., a waveform). The conductive leads 112 may include a standard insulating conductor with a flexible metallic shield and may be grounded to prevent the diffusion of the electric field generated by the conductive leads 112. The one or more outputs 114 may operate sequentially. Output parameters of the signal generator 108 may include, for example, the field strength, the frequency of the wave (e.g., a treatment frequency), and the maximum permissible 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 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, which causes the signal generator 108 to output the desired treatment frequency to the one or more transducer arrays 104.

[0048] One or more transducer arrays 104 can be configured in various shapes and positions to generate an electric field of desired configuration, orientation, and intensity at a target volume for focused therapy. One or more transducer arrays 104 can be configured to transmit two perpendicular field directions through the volume of interest.

[0049] 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 insulating ceramic discs. The electrodes 116 may be biocompatible and coupled to a flexible circuit board 118. The electrodes 116 may be configured not to come into direct contact with the skin, as the electrodes 116 are separated from the skin by a layer of conductive hydrogel (not shown) (similar to that found on electrocardiogram pads).

[0050] Electrodes 116, hydrogel, and flexible circuit board 118 can be attached to a hypoallergenic medical adhesive bandage 120 to hold 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), such as eight thermistors (accuracy ±1°C), to measure the skin temperature beneath the transducer array 104. The thermistors can be configured to measure skin temperature periodically (e.g., once per second). The thermistors can be read by control software 110 when TTFields are not necessarily transmitted to avoid any interference with temperature measurement.

[0051] If, between two subsequent measurements, the measured temperature is below the preset maximum temperature (Tmax), for example, 38.5-40.0℃ ± 0.3℃, the control software 110 can increase the current until it reaches the maximum therapeutic current (e.g., 4 amps peak-to-peak). If the temperature reaches Tmax + 0.3℃ and continues to rise, the control software 110 can decrease the current. If the temperature rises to 41℃, the control software 110 can shut down TTFields treatment and trigger an overheat alarm.

[0052] One or more transducer arrays 104 may vary in size based on the patient's body size and / or different therapeutic treatments, and may include different numbers of electrodes 116. For example, in the case of the patient's chest, each small transducer array may include 13 electrodes, while each large transducer array may include 20 electrodes, wherein the electrodes in each array are interconnected in series. For example, as Figure 2 As shown, in the case of a patient’s head, each transducer array can each include nine electrodes, with the electrodes in each array interconnected in series.

[0053] Alternative structures for one or more transducer arrays 104 can be considered and may also be used, including, for example, transducer arrays using non-disk-shaped ceramic elements, and transducer arrays using non-ceramic dielectric materials situated on multiple flat conductors. Examples of the latter include polymer films disposed on pads on a printed circuit board or on a flat metal sheet. Transducer arrays with non-capacitively coupled electrode elements may also be used. In this case, each element of the transducer array will be implemented using a region of conductive material configured to rest against the body of the subject / patient, wherein no insulating dielectric layer is 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 and / or such configuration may be used in the methods and systems described herein, provided that the transducer array (or similar device / component) configuration, arrangement, type and / or such configuration (a) enables the delivery of TTFields to the body of the subject / patient, and (b) can be positioned and / or placed on a portion of the patient / subject's body as described herein.

[0054] The status and monitoring parameters of device 100 can be stored in memory (not shown) and can be transmitted 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, treatment on, alarm, and low battery.

[0055] Figure 3A and Figure 3BAn example application of device 100 is illustrated. Transducer arrays 104a and 104b are shown, each integrated into hypoallergenic medical adhesive bandages 120a and 120b, respectively. The hypoallergenic medical adhesive bandages 120a and 120b are applied to skin surface 302. Tumor 304 is located beneath skin surface 302 and bone tissue 306, and within brain tissue 308. Electric field generator 102 causes transducer arrays 104a and 104b to generate an alternating electric field 310 within brain tissue 308, which disrupts the rapid cell division exhibited by cancer cells of tumor 304. Alternating electric field 310 has been shown to arrest and / or destroy tumor cell proliferation in non-clinical experiments. The use of alternating electric field 310 takes advantage of the specific characteristics, geometry, and division rate of cancer cells, making them susceptible to the effects of alternating electric field 310. Alternating electric fields 310 change their polarity at mid-frequency intervals (approximately 100–300 kHz). The frequency used for a specific treatment can be specific to the cell type being treated (e.g., 150 kHz for MPM). Alternating electric fields 310 have been shown to disrupt mitotic spindle microtubule assembly and lead to dielectrophoretic misalignment of intracellular macromolecules and organelles during cytokinesis. These processes result in physical disturbance of the cell membrane and programmed cell death (apoptosis).

[0056] Because the alternating electric field 310 acts directionally, cells dividing parallel to the field are more affected than cells dividing in other directions, and because cells divide in all directions, the alternating electric field 310 can be delivered through two pairs of transducer arrays 104 that generate a vertical field within the treated tumor. More specifically, one pair of transducer arrays 104 may be located on the left and right sides of the tumor (LR), while another pair of transducer arrays 104 may be located in front of and behind the tumor (AP). Circulating the alternating electric field 310 between these two directions (e.g., LR and AP) ensures maximum targeting of cell orientation. In one embodiment, the alternating electric field 310 can be delivered according to a symmetrical arrangement of the transducer arrays 104 (e.g., a total of four transducer arrays 104, two matching pairs). In another embodiment, the alternating electric field 310 can be delivered according to an asymmetrical arrangement of the transducer arrays 104 (e.g., a total of three transducer arrays 104). The asymmetric arrangement of the transducer array 104 can engage two of the three transducer arrays 104 to transmit the alternating electric field 310, and then switch to the other two of the three transducer arrays 104 to transmit the alternating electric field 310, and so on.

[0057] In vivo and in vitro studies have shown that the efficacy of TTFields therapy increases with increasing electric field strength. The described methods, systems, and devices are configured to optimize array placement on a patient's scalp to increase intensity in affected areas of the brain.

[0058] like Figure 4A As shown, the transducer array 104 can be placed on the patient's head. Figure 4B As shown, the transducer array 104 can be placed on the patient's abdomen. Figure 5A As shown, the transducer array 104 can be placed on the patient's torso. Figure 5B As shown, the transducer array 104 can be placed on the patient's pelvis. It is also contemplated that the transducer array 104 be placed on other parts of the patient's body (e.g., arms, legs, etc.).

[0059] Figure 6 This is a block diagram depicting a non-limiting example of a system 600 including a patient support system 602. The patient support system 602 may include one or more computers configured to operate and / or store an electric field generator (EFG) configuration application 606, a patient modeling application 608, and / or imaging data 610. The patient support system 602 may include, for example, computing devices. The patient support system 602 may include, for example, a laptop computer, a desktop computer, a mobile phone (e.g., a smartphone), a tablet computer, and the like.

[0060] The patient modeling application 608 can be configured to generate a three-dimensional model (e.g., a patient model) of a part of a patient's body based on imaging data 610. Imaging data 610 can 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, data that can be captured by optical instruments (e.g., photographic cameras, charge-coupled device (CCD) cameras, infrared cameras, etc.), and the like. In some implementations, the image data can include 3D data obtained from or generated by a 3D scanner (e.g., point cloud data). The patient modeling application 608 can also be configured to generate a three-dimensional array layout map based on the patient model and one or more electric field simulations.

[0061] To properly optimize array placement on a part of a patient's body, imaging data, such as MRI imaging data, can be analyzed using patient modeling application 608 to identify regions of interest, including tumors. In the case of the patient's head, to characterize how the electric field behaves and distributes within the human head, a modeling framework based on an anatomical head model simulated using the finite element method (FEM) can be used. These simulations produce realistic head models based on magnetic resonance imaging (MRI) measurements and classify tissue types within the head, such as skull, white matter, gray matter, and cerebrospinal fluid (CSF). Dielectric properties, such as relative conductivity and dielectric constant, can be assigned to each tissue type, and simulations can be run to apply different transducer array configurations to the surface of the model to understand how an externally applied electric field at a preset frequency will distribute throughout any part of the patient's body, such as the brain. Results from these simulations using paired array configurations, constant current, and a preset frequency of 200 kHz have demonstrated that the electric field distribution throughout the brain is relatively non-uniform and produces electric field strengths exceeding 1 V / cm in most tissue compartments, except for the CSF. These results were obtained assuming a peak-to-peak total current of 1800 mA at the transducer array-scalp interface. This threshold of electric field strength is sufficient to inhibit cell proliferation in glioblastoma cell lines. Furthermore, it is possible to increase the electric field strength in specific regions of the brain by almost three times by manipulating the configuration of paired transducer arrays, as shown in the example. Figure 7 As shown in the image. Figure 7 The figure shows the electric field amplitude and distribution (in V / cm) as illustrated in a coronal view of a simulation model from the finite element method. The simulation uses a configuration of transducer arrays arranged in pairs, left and right.

[0062] In one aspect, the patient modeling application 608 can be configured to determine the patient's desired (e.g., optimal) transducer array layout based on the location and extent of the tumor. For example, initial morphological measurements of head size can be determined from T1 sequences of brain MRI using axial and coronal views. Enhanced axial and coronal MRI slices can be selected to display the maximum diameter of the enhanced lesion. Using measurements of head size and distance from predetermined reference markers to the tumor margin, variations and combinations of paired array layouts can be evaluated to produce a configuration that delivers the maximum electric field intensity to the tumor site. Figure 8A As shown, the output can be a three-dimensional array layout diagram 800. For example... Figure 8B As shown, during the normal course of TTFields therapy, a three-dimensional array layout diagram 800 can be used by the patient and / or caregiver when arranging the array on the scalp.

[0063] In one aspect, the patient modeling application 608 can be configured to determine a three-dimensional array layout diagram of the patient. MRI measurements of the portion of the patient to receive the transducer array can be determined. For example, the MRI measurements can be received via a standard medical digital imaging and communication (DICOM) viewer. The MRI measurement determination can be performed automatically (e.g., through artificial intelligence technology) or manually (e.g., by a physician).

[0064] Manual MRI measurements may include receiving and / or providing MRI data via a DICOM viewer. The MRI data may include scans of portions of a patient containing a tumor. For example, in the case of a patient's head, the MRI data may include scans of the head containing 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. Figure 9A , Figure 9B , Figure 9C and Figure 9D Example MRI data showing a scan of a patient's head is shown. Figure 9A An axial T1 sequence slice containing the topmost image is shown, including tracks used to measure head size. Figure 9B Selected images of coronal T1 sequence slices at the level of the ear canal, used to measure head size, are shown. Figure 9C An enhanced T1-axial image is shown, which illustrates the maximum enhanced tumor diameter used to measure the tumor location. Figure 9D The image shows a post-contrast T1 coronal image, illustrating the diameter of the largest enhancing tumor used to measure tumor location. MRI measurements can begin with a reference marker at the outer edge of the scalp and extend tangentially from the right, anterior, and superior origin. Morphological measurements of head size can be estimated from an axial T1 MRI sequence that selects the topmost image (or the image directly above the upper edge of the track) that still includes the track.

[0065] In one aspect, MRI measurements may include one or more of, for example, head size measurements and / or tumor measurements. In another aspect, one or more MRI measurements may be rounded to the nearest millimeter and may be 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).

[0066] MRI measurements may include one or more head size measurements, such as: the maximum anterior-posterior (AP) head size measured from the outer edge of the scalp; the maximum width of the head measured perpendicular to the AP; the lateral distance from right to left; and / or the distance from the rightmost edge of the scalp to the anatomical midline.

[0067] MRI measurements can include one or more head size measurements, such as coronal head size measurements. Coronal head size measurements can be performed on images taken at the level of the ear canal. Figure 9B The head size was obtained on a T1 MRI sequence. Coronal head size measurements may include one or more of the following: a vertical measurement from the apex of the scalp to an orthogonal line depicting the lower edge of the temporal lobe; the maximum right-to-left transverse head width; and / or the distance from the rightmost edge of the scalp to the anatomical midline.

[0068] MRI measurements can include one or more tumor measurements, such as tumor location measurement. Tumor location measurement can be performed using T1-weighted post-contrast MRI sequences, first on axial images showing the diameter of the tumor at maximum enhancement. Figure 9C Tumor location measurements may include one or more of the following: maximum AP head size, excluding the nose; maximum right-to-left transverse diameter perpendicular to the AP distance measurement; distance from the right edge of the scalp to the anatomical midline; distance parallel to the right-to-left transverse distance and perpendicular to the AP measurement from the right edge of the scalp to the nearest tumor edge; distance parallel to the right-to-left transverse distance and perpendicular to the AP measurement from the right edge of the scalp to the farthest tumor edge; distance parallel to the AP measurement from the front of the head to the nearest tumor edge; and / or distance parallel to the AP measurement from the front of the head to the farthest tumor edge.

[0069] One or more tumor measurements may include coronal view tumor measurements. Coronal view tumor measurements may include post-contrast T1 MRI slices characterized by the maximum diameter of tumor enhancement. Figure 9D Coronal tumor measurements may include one or more of the following: the maximum distance from the apex of the scalp to the lower edge of the brain. In anterior sections, this will be demarcated by a horizontal line drawn at the lower edge of the frontal or temporal lobe, and posteriorly, it will extend to the lowest level of the visible tentorium; the maximum right-to-left transverse head width; 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, parallel to the right-to-left transverse distance measurement; the distance from the right edge of the scalp to the farthest tumor edge, parallel to the right-to-left transverse distance measurement; the distance from the top of the head to the nearest tumor edge, parallel to the superior apex to inferior cerebral line; and / or the distance from the top of the head to the farthest tumor edge, parallel to the superior apex to inferior cerebral line.

[0070] Other MRI measurements can be used, especially when the tumor is located in another part of the patient's body.

[0071] Patient models can be generated using MRI measurements by patient modeling application 608. In some cases, measurements can be derived from images other than MRI images, such as images (e.g., imaging data 610, etc.) derived from radiography, ultrasound, elastography, photoacoustic imaging, positron emission tomography, echocardiography, magnetic particle imaging, functional near-infrared spectroscopy, and / or the like. Any image can be used to generate a patient model. The patient model can be used to optimize TTFields treatment plans. For example, the patient model can have electrical properties assigned to various tissue types identified in the model, and the patient model can be used to determine a three-dimensional array layout diagram (e.g., a three-dimensional array layout diagram 800). Table 1 shows the standard electrical properties of tissues that can be used in the simulation.

[0072]

[0073] Continuing with the example of a tumor within a patient's head, a healthy head model can be generated, which serves as a deformable template from which a patient model can be created. When creating the patient model, the tumor can be segmented from the patient's MRI data (e.g., one or more MRI measurements). The segmented MRI data identifies the tissue type in each voxel, and electrical properties can be assigned to each tissue type based on empirical data. Regions of the tumor in the patient's MRI data can be masked, and the remaining regions of the patient's head can be registered to a 3D discrete image of the deformable template representing the healthy head model using a non-rigid registration algorithm. This process produces a non-rigid transformation that maps the healthy portion of the patient's head to the template space, and an inverse transformation that maps the template to the patient space. The inverse transformation is applied to the 3D deformable template to produce an approximation of the patient's head without the tumor. Finally, the tumor (called the region of interest (ROI)) is implanted back into the deformable template to produce the complete patient model. The patient model can be a digital representation in three-dimensional space of a part of the patient's body, including internal structures such as tissues, organs, tumors, etc.

[0074] Images derived from image data 610 (e.g., three-dimensional (3D) images, etc.) may include multiple voxels representing anatomical structures (e.g., tissues, organs, tumors, etc.) present within a patient model from which they are derived. For example, each voxel in the image may be assigned to one of three types of structures: tissue structures, region of interest (ROI) structures, and avoidance structures. Tissue type structures may define the tissue type (e.g., gray matter, white matter, skin, skull, enhancing tumor, etc.) and associated electrical properties (e.g., conductivity, relative permittivity, etc.) of the voxels within the image. ROI structures may include a set of voxels representing a patient model of interest to the user. For example, the user may want to optimize treatment by maximizing power loss associated with an electric field within a specific ROI, or may want to calculate the average field strength within a specific ROI. Avoidance region structures may include regions within the image (and / or the patient model) where transducer array placement should be avoided when simulating TTField delivery.

[0075] Patient modeling application 608 may include semi-automatic tools / functions that enable users to segment images used to create patient models and assign voxels to various structures. In some cases, patient modeling application 608 may employ rewrite logic and / or tools that enable users to segment images. 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 brush. A user can restrict the areas to which voxels are assigned. A user can select / indicate active labels for the structures to which voxels will be assigned during segmentation. A user can select / indicate smear labels for voxels that will be smeared (e.g., rewritten, etc.), such as voxels within a structure that will be assigned to the target structure defined by the active label. A user can select / indicate restrict to labels to define the structures to which segmentation should be restricted. For example, voxels that can be assigned active labels can be restricted to voxels assigned during segmentation to structures associated with smear labels and structures associated with restrict to labels. For example, if a user is using patient modeling application 608 to segment a tumor into different tissue types, the user can select "active tumor" (e.g., the segmented tumor) as the active label. Users can use the patient modeling application 608 to rewrite voxels that have not yet been assigned to tissue types, and can use the patient modeling application 608 to limit segmentation to the ROI that defines the volume of the segmented tumor. Figure 10AThe user interface 1000 of a patient modeling application 608 is illustrated. The user interface 1000 can be an interactive element of the patient modeling application 608, such as a screen, a page, and / or the like. As shown, the user has selected an active label 1010 to assign voxels of an image to structures called necrotic structures. The smear label 1020 indicates "Any Tissue," which indicates that any new voxel assignment can overwrite any voxels previously assigned to any kind of tissue structure within the image. The limit label 1030 indicates that segmentation is limited to (e.g., may only affect, may overwrite, etc.) voxels of the image assigned to the region of interest, such as "ROI 1."

[0076] In some cases, the patient modeling application 608 may include one or more interactive filling algorithms (e.g., region growing algorithms, level set algorithms, active contour-based algorithms, etc.) that segment ROIs from images based on seed voxels within the ROI (e.g., seed volume / curves, etc.). When the patient modeling application 608 initiates segmentation, the seed voxel may change shape until it captures connected regions (e.g., foreground, etc.) in the image that can be distinguished from the surrounding environment (e.g., background, etc.) based on characteristics such as grayscale levels, patterns / textures within the region, distinct edges between foreground and background, and / or similar properties. Figure 10BThe user interface 1000 of a patient modeling application 608 is illustrated. In some cases, the user interface 1000 may be an interactive element of the patient modeling application 608, enabling any / all user actions to be performed via the user interface 1000 (such as a single screen, page, and / or the like). In some cases, the user interface 1000 may include multiple screens, pages, and / or the like. When launched, the user interface 1000 may enable the user to place seeds (e.g., seed voxels, etc.) in images (such as images 1001, 1002, 1003, and 1004) using interactive tools (e.g., mouse, cursor, keyboard, etc.). When seeds are placed, ROI 1005 can be displayed in images 1001, 1002, 1003, and 1004, and automatic thresholding can be performed. The patient modeling application 608 may receive one or more instructions / signals (e.g., via interactive tools, etc.) to change the shape and / or position of the ROI, change the threshold used to separate foreground and background, delete and / or add additional seeds, and / or the like. In some cases, any seeds placed outside the ROI may be ignored. The ROI can be updated based on the seed location. The user interface 1000 enables the patient modeling application 608 to receive instructions / signals (e.g., instructions for placing / adjusting seeds, instructions for setting thresholds, instructions for changing the shape / position of the 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 region-filling algorithms in specific areas of images 1001, 1002, 1003, and 1004. For example, interactive tools can be used to mark voxels in images 1001, 1002, 1003, and 1004 and form barriers that segmented regions cannot grow beyond them. For example, if a user is using the user interface 1000 of the patient modeling application 608 to segment a resected cavity depicted in an image near a ventricle depicted in the image, voxels can be marked / selected to indicate the obstructed area located at the boundary between the resected cavity and the ventricle. Marking / selecting voxels can create boundaries that region-filling algorithms cannot pass through, ensuring that the resected cavity is correctly segmented and that the region does not flow into the ventricle. In some cases, the user interface 1000 may include one or more advanced options that enable marking / selecting voxels and / or creating one or more boundaries within the image.

[0077] In some cases, by associating all voxels associated with the first structure with the second structure, the user interface 1000 can enable the assignment of the first structure represented in the image to the second structure represented in the image. For example, as Figure 10BAs shown, at 1006, the voxel indicator / representation of the total tumor volume (GTV) associated with the ROI can be assigned to the tissue type enhancing the tumor. Voxel assignment to structures can be based on the patient modeling application 608 receiving one or more instructions / signals via any method / means. In some cases, voxel assignment to structures can be based on the patient modeling application 608 receiving one or more instructions / signals via an interactive tool, such as click (e.g., mouse click, etc.) and / or drag instructions. For example, to assign voxels belonging to a first structure to a second structure, the user can move the interactive tool on 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 of the second structure via the interactive tool (e.g., associating the coordinates of voxels of the first structure with the coordinates of voxels of the second structure, etc.). Any number of structures can be assigned to another structure. Once the image has been segmented, the 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.

[0078] The delivery of TTFields can be simulated using a patient model by the patient modeling application 608. The simulated electric field distribution, dosimetry, and simulation-based analysis are described in the publication “Correlation of Tumor treating Fields Dosimetry to Survival Outcomes in NewlyDiagnosed Glioblastoma: A Large-Scale Numerical Simulation-based Analysis of Data from the Phase 3 EF-14 randomized Trial” by U.S. Patent Publication No. 20190117956 A1 and Ballo et al. (2019), which is incorporated herein by reference in its entirety.

[0079] To ensure the systematic localization of the transducer array relative to the tumor location, a reference coordinate system can be defined. For example, the lateral plane can initially be defined by the conventional LR and AP localization of the transducer array. The left-right direction can be defined as the x-axis, the AP direction can be defined as the y-axis, and the head-to-tail direction perpendicular to the XY plane can be defined as the Z-axis.

[0080] After defining the coordinate system, the transducer arrays can be virtually placed on the patient model, with their centers and longitudinal axes in the XY plane. A pair of transducer arrays can be systematically rotated about the z-axis of the head model, i.e., from 0 degrees to 180 degrees in the XY plane, thus covering the entire circumference of the head (through symmetry). The rotation interval can be, for example, 15 degrees, corresponding to a translation of approximately 2 cm, thus giving a total of twelve different positions within the 180-degree range. Other rotation intervals can be considered. The electric field distribution can be calculated for each transducer array position relative to the tumor coordinates.

[0081] The electric field distribution in the patient model can be determined using the finite element (FE) approximation of the potential by the patient modeling application 608. Typically, the quantities defining a time-varying electromagnetic field are given by the complex Maxwell's equations. However, in biological tissue, and at the low to mid-frequency (f = 200 kHz) of TTFields, the electromagnetic wavelength is much larger than the size of the head, and the dielectric constant ε is negligible compared to the real-valued conductivity σ, i.e., ω = 2πf is the angular frequency. This means that electromagnetic propagation effects and capacitive effects in the tissue are negligible, and therefore the scalar potential can be well approximated by the static Laplace equation ∇∙(σ∇φ) = 0, which has appropriate boundary conditions at the electrodes and skin. Therefore, the complex impedance is considered resistive (i.e., reactance is negligible), and thus the current flowing within the volume conductor is primarily a free (ohmic) current. The FE approximation of the Laplace equation is calculated using SimNIBS software (simnibs.org). The calculations were based on the Galerkin method, with a residual requirement of <1E-9 for the conjugate gradient solver. Dirichlet boundary conditions were used, where the potential at each electrode array was set to a (arbitrarily chosen) fixed value. The electric (vector) field was calculated as the numerical gradient of the potential, and the current density (vector field) was calculated from the electric field using Ohm's law. The potential difference between the electric field value and the current density was linearly rescaled to ensure a total peak-to-peak amplitude of 1.8 A for each array pair, calculated as the (numerical) surface integral of the normal current density components on all triangular surface elements of the active electrode disk. This corresponds to the current level of the Optune® device used for clinical TTFields therapy. The “dose” of TTFields was calculated as the intensity (L2 norm) of the field vector. It was assumed that the modeling current was provided by two separate and sequentially active sources, each connected to a pair of 3×3 transducer arrays. The left and rear arrays can be defined as sources in the simulation, while the right and front arrays are the corresponding sinks. However, since TTFields uses alternating fields, this choice is arbitrary and does not affect the results.

[0082] Patient modeling application 608 can determine the average electric field strength generated by a transducer array placed at multiple locations on a patient for one or more tissue types. In one aspect, the transducer array location corresponding to the highest average electric field strength in one or more tumor tissue types can be selected as the patient's desired (e.g., optimal) transducer array location. In another aspect, as a result of the patient's physical condition, one or more candidate locations of the transducer array(s) can be excluded. For example, one or more candidate locations can be excluded based on areas of skin irritation, scars, surgical sites, discomfort, etc. Therefore, after excluding one or more candidate locations, the transducer array location corresponding to the highest average electric field strength in one or more tumor tissue types can be selected as the patient's desired (e.g., optimal) transducer array location. Thus, a transducer array location that results in a less than the maximum possible average electric field strength can be selected.

[0083] The patient model can be modified to include indications of the desired transducer array locations. The resulting patient model, including indications of one or more desired transducer array locations, can be referred to as a three-dimensional array layout diagram (e.g., three-dimensional array layout diagram 600). Thus, the three-dimensional array layout diagram can include digital representations of portions of the patient's body in three-dimensional space, indications of tumor locations, indications of locations for placing one or more transducer arrays, combinations thereof, and the like.

[0084] The three-dimensional array layout diagram can be provided to the patient in digital and / or physical form. The patient and / or patient caregiver can use the three-dimensional array layout diagram to attach one or more transducer arrays to an associated part of the patient's body (e.g., the head).

[0085] Figure 11 This is a block diagram depicting an environment 1100 including a non-limiting example of a patient support system 104. In one aspect, some or all of the steps of any of the described methods can be performed on a computing device as described herein. The patient support system 104 may include one or more computers configured to store an EFG configuration application 606, a patient modeling application 608, imaging data 610, and one or more of such devices.

[0086] The patient support system 104 may be a digital computer, which, in terms of hardware architecture, typically includes 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 1016 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 1016 may have additional elements, such as controllers, buffers (caches), drivers, repeaters, and receivers, to enable communication; these additional elements are omitted for simplicity. Furthermore, the local interface may include address, control, and / or data connections to enable proper communication between the aforementioned components.

[0087] Processor 1109 can be a hardware device for executing software, particularly software stored in memory system 1120. Processor 1109 can be any custom-made or commercially available processor, central processing unit (CPU), auxiliary processor among several processors associated with patient support system 104, semiconductor-based microprocessor (in the form of a microchip or chipset), or any device generally used for executing software instructions. When patient support system 1002 is in operation, processor 1109 can be configured to execute software stored in memory system 1120, transfer data to and from memory system 1120, and control the operation of patient support system 104 according to the overall software.

[0088] I / O interface 1112 can be used to receive user input from one or more devices or components and / or to provide system output to one or more devices or components. User input can be provided via, for example, a keyboard and / or a mouse. System output can be provided via a display device and a printer (not shown). I / O interface 1112 may 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.

[0089] Network interface 1114 can be used for transmitting and receiving data from patient support system 104. 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. Network interface 1114 may include address, control, and / or data connections to enable appropriate communication.

[0090] The memory system 1120 may include any or a combination 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 disk drive, magnetic tape, CD-ROM, DVD-ROM, etc.). Furthermore, the memory system 1120 may incorporate electronic, magnetic, optical, and / or other types of storage media. Note that the memory system 1120 may have a distributed architecture, where various components are geographically dispersed but accessible to the processor 1109.

[0091] The software in the memory system 1120 may include one or more software programs, each of which includes an ordered list of executable instructions for implementing logical functions. Figure 11 In the example, the software in the memory system 1120 of the patient support system 104 may include 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.

[0092] For illustrative purposes, applications and other executable program components (such as operating system 1118) are illustrated herein as discrete boxes; however, it should be understood that such programs and components may reside in different storage components of patient support system 104 at different times. Implementations of EFG configuration application 606, patient modeling application 608, imaging data 610, and / or control software 1122 may be stored on or transferred across some form of computer-readable medium. Any method disclosed in the methods may be executed by computer-readable instructions implemented 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 intended to be limiting, a computer-readable medium may include "computer storage medium" and "communication medium." A "computer storage medium" may include volatile and non-volatile, removable and non-removable media implemented with any method or technique for storing information such as computer-readable instructions, data structures, program modules, or other data. Exemplary computer storage media may include RAM, ROM, EEPROM, flash memory or other storage technologies, CD-ROM, digital multifunction disc (DVD) or other optical storage devices, cassette tape, magnetic tape, disk storage devices or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer.

[0093] exist Figure 12In the embodiments illustrated herein, one or more of the device 100, patient support system 602, patient modeling application 608, and / or any other device / component described herein may be configured to perform method 1200, including determining a three-dimensional (3D) model at 1210, wherein the 3D model comprises a plurality of voxels.

[0094] At 1220, an instruction for selecting an active label is received, wherein selecting an active label enables a user indicator to associate a voxel among a plurality of voxels with which it interacts with the active label. The user indicator may include a mouse, a keyboard, a haptic response interface (e.g., a touchscreen, etc.), and / or one or more of the like.

[0095] At 1230, an instruction for selecting a smear label is received, wherein the selection of a smear label causes voxels among a plurality of voxels not associated with the smear label to be write-protected so as not to be associated with the active label.

[0096] At 1240, an instruction is received to select a restriction to a label for a structure within a specified 3D model, wherein the selection of the restriction to a label causes voxels among the plurality of voxels not associated with the structure within the 3D model to be write-protected so as not to be associated with an active label.

[0097] At 1250, based on the interaction with one or more voxels via the user indicator, one or more voxels among the multiple voxels associated with the smear label and the restriction to the label are associated with the active label.

[0098] In some cases, method 1200 may include displaying a 3D model.

[0099] In some cases, method 1200 may include receiving an interaction with one or more other voxels among a plurality of voxels via a user indicator, determining that the other voxels are associated with an application label rather than a restriction to a label, and ignoring the interaction with the other voxels based on the determination that the other voxels are associated with an application label rather than a restriction to a label.

[0100] In some cases, method 1200 may include receiving an interaction with one or more other voxels among the plurality of voxels via a user indicator, determining that the other one or more voxels are not associated with an application label or a restriction label, and ignoring the interaction with the other one or more voxels based on the determination that the other one or more voxels are not associated with an application label or a restriction label.

[0101] In some cases, method 1200 may include receiving an interaction with one or more other voxels among the plurality of voxels via a user indicator, determining that the other voxels are associated with a restriction label and not with a smear label, and ignoring the interaction with the other voxels based on the determination that the other voxels are associated with a restriction label and not with a smear label.

[0102] exist Figure 13 In the embodiments illustrated herein, one or more of device 100, patient support system 602, patient modeling application 608, and / or any other device / component described herein may be configured to perform method 1300, including determining a three-dimensional (3D) model at 1310, wherein the 3D model comprises a plurality of voxels, wherein each of the plurality of voxels is associated with an illumination intensity value, wherein each of the plurality of voxels is associated with a foreground represented by the 3D model or a background represented by the 3D model based on the corresponding illumination intensity value. In some cases, the illumination intensity value may be associated with RGB color.

[0103] At 1320, an interactive element is displayed. In some cases, the interactive element may be a single screen, a page, and / or the like, which enables all interactive steps of method 1300 to be performed via a single screen, page, and / or the like.

[0104] At 1330, an instruction for the selection of a seed voxel is received via an interactive element, wherein the seed voxel is associated with a specific value of illumination intensity.

[0105] At 1340, one or more voxels are identified as having illumination intensity values ​​within a threshold range of specific values, wherein the illumination intensity values ​​of one or more voxels within the threshold range of specific values ​​are associated with a region of interest (ROI) within the 3D structure.

[0106] At 1350, this causes one or more changes in the illumination intensity values ​​of one or more voxels associated with the ROI and one or more changes in the illumination intensity values ​​of one or more voxels among a plurality of voxels.

[0107] At 1360, changes in the represented shape of the ROI and changes in the position of the ROI within the 3D model are caused by changes in the illumination intensity values ​​of one or more voxels associated with the ROI and changes in the illumination intensity values ​​of one or more voxels among a plurality of voxels.

[0108] At 1370, based on the interaction with the interactive element via the user indicator, one or more of steps 1330-1360 are repeated.

[0109] At 1380, based on another interaction via a user indicator and an interactive element, one or more voxels of a plurality of voxels are associated with a boundary within the ROI, wherein the illumination intensity value of one or more voxels associated with the boundary matches a specific value.

[0110] At 1390, match the illumination intensity value of one or more voxels within the boundary to a specific value.

[0111] exist Figure 14 In the embodiments illustrated herein, one or more of device 100, patient support system 602, patient modeling application 608, and / or any other device / component described herein may be configured to perform method 1400, including determining a three-dimensional (3D) model at 1410, wherein the 3D model comprises a plurality of voxels, wherein each of the plurality of voxels is associated with coordinates within the 3D model.

[0112] At 1420, the structure within the 3D model is determined, wherein the structure comprises one or more voxels among a plurality of voxels.

[0113] At 1430, another structure within the 3D model is determined, wherein the other structure includes one or more voxels among the plurality of voxels.

[0114] At 1440, receive instructions for selecting one or more voxels.

[0115] At 1450, based on the instruction to select one or more other voxels, a request to change the coordinates of the other one or more voxels is received.

[0116] At 1460, based on the request, the coordinates of the other one or more voxels are changed, wherein changing the coordinates associates the other structure with the structure.

[0117] In view of the described apparatuses, systems, and methods and their variations, certain more specifically described embodiments of the invention are described below. However, these specifically enumerated embodiments should not be construed as limiting any of the different claims that incorporate the different or more general teachings described herein, or as limiting the “specific” embodiments to some means beyond the inherent meaning of the language used literally therein.

[0118] Example 1: A method comprising: determining a three-dimensional (3D) model, wherein the 3D model includes a plurality of voxels; receiving an instruction to select an active label, wherein the selection of the active label enables a user indicator to associate voxels among the plurality of voxels with which it interacts with the active label; receiving an instruction to select a smear label, wherein the selection of the smear label causes voxels among the plurality of voxels not associated with the smear label to be write-protected so as not to be associated with the active label; receiving an instruction to select a constraint-to-label for a structure within a specified 3D model, wherein the selection of the constraint-to-label causes voxels among the plurality of voxels not associated with the structure within the 3D model to be write-protected so as not to be associated with the active label; and, based on interaction with one or more voxels via a user indicator, associating one or more voxels among the plurality of voxels associated with the smear label and the constraint-to-label with the active label.

[0119] Example 2: An embodiment as described in any of the foregoing embodiments, further comprising displaying a 3D model.

[0120] Example 3: An embodiment as described in any of the foregoing embodiments, wherein the user indicator includes one or more of a mouse, keyboard, or haptic response interface.

[0121] Example 4: An embodiment as described in any of the foregoing embodiments, further comprising: receiving an interaction with one or more other voxels among a plurality of voxels via a user indicator; determining that the other one or more voxels are associated with an application label rather than a restriction label; and ignoring the interaction with the other one or more voxels based on the determination that the other one or more voxels are associated with the application label rather than the restriction label.

[0122] Example 5: An embodiment as described in any of Examples 1-3, further comprising: receiving an interaction with one or more other voxels among a plurality of voxels via a user indicator; determining that the other one or more voxels are not associated with the smear label or the restriction label; and ignoring the interaction with the other one or more voxels based on the determination that the other one or more voxels are not associated with the smear label or the restriction label.

[0123] Example 6: An embodiment as described in any of Examples 1-3, further comprising: receiving an interaction with one or more other voxels among a plurality of voxels via a user indicator; determining that the other one or more voxels are associated with the restriction label and not with the smear label; and ignoring the interaction with the other one or more voxels based on the determination that the other one or more voxels are associated with the restriction label and not with the smear label.

[0124] Example 7: A method comprising: (a) determining a three-dimensional (3D) model, wherein the 3D model comprises a plurality of voxels, wherein each of the plurality of voxels is associated with an illumination intensity value, wherein each of the plurality of voxels is associated with a foreground or a background of a representation of the 3D model based on the corresponding illumination intensity value; (b) causing an interactive element to be displayed; (c) receiving an instruction for selection of a seed voxel via the interactive element, wherein the seed voxel is associated with an illumination intensity of a specific value; (d) determining that one or more voxels of the plurality of voxels have illumination intensity values ​​within a threshold range of the specific values, wherein the illumination intensity values ​​of one or more voxels of the plurality of voxels within the threshold range of the specific values ​​are associated with a region of interest (ROI) within a 3D structure; and (e) causing an illumination intensity of one or more voxels associated with the ROI. (c) changes in the value and changes in the illumination intensity value of one or more voxels of a plurality of voxels; (f) causing changes in the represented shape of the ROI and changes in the position of the ROI within the 3D model based on changes in the illumination intensity value of one or more voxels associated with the ROI and changes in the illumination intensity value of one or more voxels of a plurality of voxels; (g) repeating one or more of steps (c)-(f) based on interaction with an interactive element via a user indicator; (h) associating one or more voxels of a plurality of voxels with a boundary within the ROI based on another interaction with an interactive element via a user indicator, wherein the illumination intensity value of one or more voxels associated with the boundary matches a specific value; and (i) matching the illumination intensity value of one or more voxels within the boundary with a specific value.

[0125] Example 8: An example as described in Example 7, wherein the illumination intensity value is associated with RGB color.

[0126] Example 9: A method comprising: determining a three-dimensional (3D) model, wherein the 3D model includes a plurality of voxels, wherein each voxel of the plurality of voxels is associated with coordinates within the 3D model; determining a structure within the 3D model, wherein the structure includes one or more voxels of the plurality of voxels; determining another structure within the 3D model, wherein the other structure includes another one or more voxels of the plurality of voxels; receiving an instruction to select the other one or more voxels; based on the instruction to select the other one or more voxels, receiving a request to change the coordinates of the other one or more voxels; and based on the request, changing the coordinates of the other one or more voxels, wherein changing the coordinates associates the other structure with the structure.

[0127] Unless otherwise expressly stated, it is not intended to interpret any method described herein as requiring its steps to be performed in a particular order. Therefore, where a method claim does not actually describe the order in which its steps are followed, or where the claims or description do not otherwise specifically specify that the steps will be limited to a particular order, it is not intended to infer the order in any way. This applies to any possible non-explicit basis of interpretation, including: logical questions concerning the arrangement of steps or operational flows; simple meanings derived from grammatical organization or punctuation; and the number or type of embodiments described in the specification.

[0128] While methods and systems have been described in conjunction with preferred embodiments and specific examples, they are not intended to limit the scope to the particular embodiments illustrated, as the embodiments herein are intended in all respects to be illustrative rather than restrictive.

[0129] Unless otherwise expressly stated, it is not intended to interpret any method described herein as requiring its steps to be performed in a particular order. Therefore, where a method claim does not actually describe the order in which its steps are followed, or where the claims or description do not otherwise specifically specify that the steps will be limited to a particular order, it is not intended to infer the order in any way. This applies to any possible non-explicit basis of interpretation, including: logical questions concerning the arrangement of steps or operational flows; simple meanings derived from grammatical organization or punctuation; and the number or type of embodiments described in the specification.

[0130] Various modifications and variations will be possible for those skilled in the art without departing from the scope or spirit. Other embodiments will be apparent to those skilled in the art upon consideration of the specification and practice disclosed herein. The specification and examples are intended to be illustrative only, wherein the true scope and spirit are indicated by the appended claims.

Claims

1. A method comprising: Define a three-dimensional (3D) model, wherein the 3D model comprises multiple voxels; Receives an instruction to select an activity label, wherein the selection of the activity label enables the user indicator to associate a voxel among the multiple voxels with which it interacts with the activity label. Receive an instruction to select a smear label, wherein the selection of a smear label causes voxels among a plurality of voxels not associated with the smear label to be write-protected so as not to be associated with the active label. Receive an instruction to select a restriction to a label for a structure within a specified 3D model, wherein the selection of the restriction to a label causes voxels among a plurality of voxels not associated with the structure within the 3D model to be write-protected so as not to be associated with an active label; and Based on the interaction with one or more voxels via a user indicator, one or more voxels among a plurality of voxels associated with smearing labels and limiting to labels are associated with active labels.

2. The method of claim 1, further comprising displaying a 3D model.

3. The method according to claim 1, wherein, The user indicator includes one or more of a mouse, keyboard, or haptic response interface.

4. The method according to claim 1, further comprising: Receive interactions with one or more other voxels among a plurality of voxels via a user indicator; Determine that the other one or more voxels are associated with the application label, rather than being limited to the label; and Based on the determination that the other one or more voxels are associated with the smear label rather than the restriction to the label, interactions with the other one or more voxels are ignored.

5. The method of claim 1, further comprising: Receive interactions with one or more other voxels among a plurality of voxels via a user indicator; Determine that the other one or more voxels are not associated with the application label or the restriction label; and Based on the determination that the other one or more voxels are not associated with the smear label or the restriction to label, interactions with the other one or more voxels are ignored.

6. The method of claim 1, further comprising: Receive interactions with one or more other voxels among a plurality of voxels via a user indicator; Determine that the other one or more voxels are associated with the restriction label and not with the smear label; and Based on the determination that the other one or more voxels are associated with the restriction label and not with the smear label, interactions with the other one or more voxels are ignored.

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

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

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