Automatic generation of regions of interest in medical images for treatment planning in tumor treatment fields.

Abstract

A computer implementation method for a treatment plan for administering a tumor treatment field to a subject, the method comprising: presenting a slice of a medical image of the subject containing voxels on a display; determining the segmentation of the minimum number of voxels of at least one tissue type in the slice of the medical image; receiving a user selection for automatically generating a region of interest (ROI) in the medical image for applying the tumor treatment field to the subject; and automatically generating the region of interest in the medical image for applying the tumor treatment field to the subject based on the segmentation of the minimum number of voxels of at least one tissue type in the slice of the medical image.

Description

Technical Field

[0001] Cross - reference to related applications This application claims the priority of U.S. Provisional Application No. 63 / 609,202 filed on December 12, 2023 and U.S. Provisional Application No. 63 / 524,470 filed on June 30, 2023, the contents of which are hereby incorporated by reference in their entirety. This application claims the priority of U.S. Patent Application No. 18 / 750,582 filed on June 21, 2024, U.S. Patent Application No. 18 / 675,714 filed on May 28, 2024, and U.S. Provisional Application No. 63 / 523,853 filed on June 28, 2023, the contents of which are hereby incorporated by reference in their entirety.

Background Art

[0002] Tumor treatment fields (TT fields) are low - intensity alternating electric fields within the intermediate frequency range (e.g., 50 kHz to 1 MHz) and can be used in the treatment of tumors as described in U.S. Patent No. 7,565,205. In current commercial systems, a TT field is non - invasively induced in the region of interest by applying an alternating current (AC) voltage between an electrode assembly (e.g., an array of capacitive coupling electrodes, also referred to as an electrode array, a transducer array, or simply a "transducer") placed on a patient's body. Conventionally, a first pair of transducers and a second pair of transducers are placed on a subject's body. An AC voltage is applied between the first pair of transducers for a first time interval, generating an electric field with electric field lines running generally in the anterior - posterior direction. Next, an AC voltage is applied between the second pair of transducers at the same frequency for a second time interval, generating an electric field with electric field lines running generally in the left - right direction. The system repeats this two - step sequence over the course of treatment.

Brief Description of the Drawings

[0003] [Figure 1] It is a flowchart showing an example of a computer - implemented method for a treatment plan to administer a TT field to a subject. [Figure 2] This flowchart shows an example of a computer implementation method for automatically generating ROIs in medical images of a subject. [Figure 3] This flowchart shows an example of a computer implementation method for generating a transducer layout for applying a TT field to a subject. [Figure 4] This shows an example of a user interface for an application that manually segments medical images of a subject. [Figure 5] This shows an example of a user interface for an application that automatically generates regions of interest in medical images of a patient. [Figure 6] This shows an example of a user interface for a warning regarding the automatic generation of regions of interest in medical images of a subject. [Figure 7] Figures 7A to 7C are examples showing various stages of medical images of subjects with automatically generated ROIs within the medical images. [Figure 8] An example of a system for applying an alternating electric field to a subject is shown. [Figure 9] An example of a transducer placed on the subject's head is shown. [Figure 10] An example of a computer device according to one or more embodiments described herein is shown.

[0004] Various embodiments will be described in detail below with reference to the attached drawings. In the drawings, similar reference numerals represent similar elements. [Modes for carrying out the invention]

[0005] This application describes exemplary techniques for a treatment plan for administering a TT field to a subject.

[0006] When developing a treatment plan for administering TT field to a subject, it may be necessary to identify a region of interest (ROI) within the subject in order to determine the dosage of TT field to administer. Typically, ROI identification in a subject is performed by a user who manually reviews the subject's medical images. These medical images may contain numerous slices and, furthermore, numerous voxels. For example, the user may review each slice of the subject's medical image and manually identify the ROI within each slice. The ROI of a medical image can be a combination of the ROIs of each slice. This manual procedure is time-consuming for the user and leads to increased costs. This manual procedure can also cause delays in TT field treatment planning while the user is scheduled to review and manually process the subject's medical images. Furthermore, if a subject has multiple medical images to be used in TT field treatment planning, it may be necessary to review each image, requiring the user to manually identify the ROI in each image. This further increases the user's workload, the associated costs, and can cause further delays in TT field treatment planning.

[0007] One or more embodiments described herein provide technical solutions to address the technical problem of identifying ROIs within a subject in order to determine the dosage of TT field to be administered to the subject. In particular, the inventors have discovered a technique for automatically generating ROIs in medical images for TT field treatment planning. This advanced technique eliminates the need for the user to manually examine medical images or individually examine each slice of a medical image to determine ROIs. Instead, in some embodiments, the user only needs to segment a small number of voxels within the medical image, and the computer automatically generates the ROIs of the medical image. By using the advanced techniques of the present invention, the user effort required to generate ROIs in medical images can be significantly reduced compared to conventional manual procedures, and as a result, the cost of these advanced techniques can also be reduced compared to conventional manual procedures. Furthermore, by using these advanced techniques, delays in TT field treatment planning that require scheduling adjustments for users to manually examine and process medical images of subjects can be significantly reduced. This is because the time required for users to process medical images is significantly reduced by using these advanced techniques. Furthermore, if a subject has multiple medical images to be used for TT field treatment planning, the time required by the user to generate an ROI for each medical image according to the technique of the present invention can be significantly reduced compared to the time required by a user employing conventional manual procedures. This can result in significant associated cost savings and reduced delays in TT field treatment planning. Due to the volume of data and computational complexity, the technical solution is not feasible with human intelligence and must instead be performed by the computer-based method described herein.

[0008] The embodiments described herein further provide practical applications for generating transducer layouts for delivering TT field therapy to a subject by avoiding the need to manually generate regions of interest in the subject's medical images. These advanced techniques avoid the conventional manual procedure for generating regions of interest in the subject's medical images, resulting in time savings, cost reductions, and reduced delays in providing TT field therapy plans for users (e.g., healthcare professionals). These and other technical improvements can be achieved using one or more embodiments described herein.

[0009] In some embodiments, computer-based advanced techniques may include the automatic generation of ROIs in medical images for TT field treatment planning. For example, the user may first manually segment a minimum number of voxels (e.g., 10 voxels) for at least one tissue type (e.g., tumor, resection, necrotic area) within the medical image, and then select the "Automatic ROI Generation" button on the user interface. In some embodiments, the user may select a margin value (e.g., proximal boundary zone (PBZ)) to be added to each segmented region of the medical image. Then, based on the manually segmented voxels, the computer may automatically determine the segmented regions for each tissue type, add margins to each segmented region to obtain expanded segmented regions, and combine the expanded segmented regions to obtain a region of interest in the medical image.

[0010] Figure 1 shows an exemplary computer implementation method 100 for a treatment plan for administering TT field to a subject. Specific steps of method 100 are described as computer implementation steps. Method 100 can be implemented by any suitable system or apparatus, such as apparatus 1000 in Figure 10. While the sequence of operations is shown in Figure 1 for illustrative purposes, the timing and sequence of these operations may be modified as appropriate without prejudice to the purpose and merits of the examples detailed herein.

[0011] In step 102, method 100 may include presenting slices of a medical image of the subject on a display. In some embodiments, the medical image comprises voxels and includes one or more slices. The medical image may include at least one computed tomography (CT) medical image, magnetic resonance imaging (MRI) medical image, or positron emission tomography (PET) medical image. In some embodiments, the medical image of the subject may include images of the subject's torso and / or head.

[0012] In step 104, method 100 may include determining the minimum voxel number segmentation of at least one tissue type in a slice of a medical image. This at least one tissue type may include at least one of tumor, gross tumor volume (GTV), resection cavity, necrotic area, or enhanced tumor. GTV may represent macroscopic tumor volume and may be used as a central target volume in treatment. Resection cavity may refer to a cavity resulting from the removal of tissue, structure, or organ. Necrotic area may refer to dead cells due to injury or disease. Enhanced tumor may refer to a residual tumor area to which a contrast agent has been injected, making the area more detectable in medical images (e.g., MRI images). In some embodiments, a user interface may be provided to the user to determine the segmentation of at least one tissue type in a slice of a medical image. As an example, Figure 4 shows an example of a user interface for an application for manually segmenting a medical image of a subject.

[0013] In some embodiments, step 104 may be based on user input. For example, the user may select voxels as a specific tissue type in a slice of a medical image, and these user-selected voxels may be identified as a determined segmentation of the minimum number of voxels of at least one tissue type in a slice of a medical image. For example, the minimum number of voxels in a medical image for segmentation may be between 5 and 25 voxels. For example, the minimum number of voxels in a medical image for segmentation may be 10 voxels. In some embodiments, if the minimum number of voxels in a medical image for segmentation is not met, the user may be given a warning.

[0014] In step 106, method 100 may include receiving a user selection to automatically generate an ROI in a medical image in order to apply a TT field to a subject. In some embodiments, the user can select a button in the user interface (e.g., the “Automatic ROI Generation” button 502 shown in Figure 5), and once this selection is received, an ROI is automatically generated in the medical image. In some embodiments, the user may input a margin to add to the segmented region (e.g., a proximal boundary region (PBZ)) as part of the automatic generation of the ROI.

[0015] In step 108, method 100 may include performing automated generation of ROIs in a medical image for applying a TT field to a subject, based on the minimum voxel count segmentation of at least one tissue type within a slice of the medical image in step 104. In some embodiments, the segmented regions in the medical image may be automatically generated based on the minimum voxel count segmentation of at least one tissue type in step 104. In some embodiments, the automated segmentation may be performed using the technology of the commonly owned U.S. Patent Application Publication No. 2021 / 0201572, titled "METHODS, SYSTEMS, AND APPARATUSES FOR IMAGE SEGMENTATION". The contents of that application are incorporated herein by reference in their entirety. In some embodiments, the ROI may include clinical tumor volume (CTV). CTV may refer to tissue volume including macroscopic tumor volume (GTV) and asymptomatic microscopic malignant lesions. In some embodiments, generating ROIs in medical images may include adding a proximal boundary region (PBZ) to the GTV to obtain a CTV in the medical image. In some embodiments, a user interface may be provided to the user to initiate automatic ROI generation. As an example, Figure 5 shows an example of a user interface for an application that automatically generates regions of interest (ROIs) in medical images of a subject.

[0016] In some embodiments, the automated generation of ROIs in step 108 may include determining a segmented region of a tissue type in a medical image based on segmentation based on the minimum number of voxels of at least one tissue type in a slice of the medical image, determining a PBZ for the segmented region of the medical image, and then adding the PBZ to the segmented region of the medical image to obtain an ROI in the medical image for applying a TT field to the subject. In particular, the segmented region may be a tumor, GTV, resection cavity, necrotic region, enhanced tumor, or non-enhanced tumor. In this specification, the term “segmented region” may refer to a three-dimensional volume in a medical image. In some embodiments, step 106 may be carried out using method 200 shown in Figure 2, which will be described later.

[0017] In some embodiments, when automatically generating ROIs, calculation warnings may be provided via a user interface. In some embodiments, a user interface may be provided to the user to initiate the automatic generation of ROIs. Figure 6 shows an example of a user interface for an application that automatically generates ROIs in a medical image of a subject. In some embodiments, a warning may indicate that the minimum number of voxels was not selected (for example, in step 104), and therefore the automatic generation of ROIs cannot proceed. In some embodiments, a warning may indicate that the ROI in the medical image does not contain the minimum number of gray matter voxels and / or white matter voxels, that the ROI in the medical image does not contain the minimum number of enhancing tumor voxels, that only regions of interest having gray matter voxels and / or white matter voxels or enhancing tumor voxels are presented, and / or at least one of similar elements including combinations and / or multiples thereof. If one or more of these warnings are received, the computer system may provide the user with an opportunity to resolve the issue. For example, to address a warning that a medical image's ROI does not contain a minimum number of gray matter and / or white matter voxels, the user may need to resize the segmentation in step 104 to include additional gray matter and / or white matter voxels. For example, to address a warning that a medical image's ROI does not contain a minimum number of enhancing tumor voxels, the user may need to resize the segmentation in step 104 to include additional enhancing tumor voxels. For example, to address a warning that only regions of interest containing gray matter and / or white matter voxels, or enhancing tumor voxels, are presented, the user may need to resize the segmentation in step 104 so that voxels not within the ROI are presented.

[0018] In step 110, method 100 may include generating a plurality of transducer layouts for applying a TT field to a subject based on an automatically generated ROI within a medical image. In some embodiments, at least one transducer arrangement may include two pairs of transducers for placement at transducer positions on the subject. In some embodiments, at least one transducer layout may include one pair of transducers for placement at transducer positions on the subject. In some embodiments, the transducer layout may be generated based on one or more of a three-dimensional model of the subject, a three-dimensional model of a general subject, or geometric calculations of the subject. In some embodiments, step 110 may be implemented using method 300 shown in FIG. 3, which will be described later.

[0019] FIG. 2 shows a flowchart of method 200 for automatically generating an ROI in a medical image of a subject. Method 200 may be used to implement step 108 of FIG. 1. Specific steps of method 200 may be described as computer-implemented steps. Method 200 may be implemented by any suitable system or device, such as device 1000 of FIG. 10. In FIG. 2, the order of operations is shown for illustrative purposes, but the timing and order of such operations may be changed as appropriate without negating the objectives and advantages of the examples detailed herein.

[0020] In step 202, method 200 may include determining at least two segmented regions of different tissue types within the medical image based on segmentation of the minimum number of voxels of at least one tissue type in a slice within the medical image. For example, the at least two segmented regions may include at least two of a tumor, gross tumor volume, resection cavity, necrosis region, or enhanced or non-enhanced tumor. In step 104, the user may select a segmentation of the medical image having the minimum number of voxels.

[0021] In step 204, method 200 may include determining a margin for each segmented region in the medical image. The margin may be used to expand the segmented region and may be useful when the location of segmentation in the subject is uncertain (e.g., voxels on the boundary or voxels separating tumor tissue from non-tumor tissue). In some embodiments, the margin may be the PBZ for each segmented region in the intermediate image. In some embodiments, the margin (or PBZ) may be the same or different values for each segmented region in the medical image. In some embodiments, the margin (or PBZ) may be a user-selectable value for the segmented regions in the medical image. In some embodiments, the margin may be a distance measurement. By way of example, the margin may be between 1 mm and 15 mm, or between 1 mm and 20 mm. By way of example, the margin may be 3 mm. In some embodiments, the margin may be a percentage of the size of the segmented region. By way of example, the margin may be between 0.1% and 5.0% of the size of the segmented region, or between 0.01% and 10.0% of the size of the segmented region. By way of example, the margin may be 0.01%, 0.1%, 0.5%, or 1.0%.

[0022] In step 206, method 200 may include, for each segmented region, adding a margin to the segmented region of the medical image to obtain an extended segmented region. In some embodiments, each extended segmented region corresponds to a segmented region.

[0023] In step 208, method 200 may include combining the extended segmented regions to obtain a ROI in the medical image for applying a TT field to the subject. The ROI may include overlapping and non-overlapping portions of the extended segmented regions.

[0024] In some embodiments, the order of steps 206 and 208 may be reversed. For example, the segmented regions may first be merged into a combined segmented region, and then margins may be added to the combined segmented region to obtain an ROI in the medical image.

[0025] As an example of implementing an embodiment of Method 200, Figures 7A to 7C show examples of various stages of medical images of a subject with an automatically generated ROI within the medical image.

[0026] Figure 3 is a flowchart of an exemplary method 300 for generating a transducer layout for applying a TT field to a subject. Method 300 may be used to carry out step 110 in Figure 1. Certain steps of Method 300 may be described as computer implementation steps. Method 300 may be carried out by any suitable system or apparatus, such as apparatus 1000 in Figure 10. In Figure 3, the sequence of operations is shown for illustrative purposes, but the timing and sequence of these operations may be changed where appropriate without prejudice to the purpose and merits of the examples detailed herein.

[0027] In step 302, method 300 may include creating a three-dimensional (3D) model of the subject, including an automatically generated ROI, based on medical images. In some embodiments, the 3D model may include a 3D conductivity map. The 3D conductivity map may represent the conductivity of the subject's body tissues. In some embodiments, the creation of the 3D model may include performing calculations to determine the conductivity of the subject's tissues based on medical images and the tissue types in the medical images. As an example, the creation of the 3D model may include assigning tissue types and associated conductivity to voxels in the subject's 3D model. In some embodiments, the creation of the subject's 3D model may include automatically segmenting normal tissue in the medical images. In some embodiments, after the subject's 3D model has been created, method 300 may include receiving user approval for the three-dimensional conductivity map associated with the 3D model. In some embodiments, automatically segmented normal tissues, such as gray matter, white matter, skull, scalp, and cerebrospinal fluid (CSF), may be automatically added to the 3D model. In some embodiments, the creation of a 3D model of the subject in step 302 can be carried out using the technology of U.S. Patent Application Publication No. 2021 / 0201572, entitled “METHODS, SYSTEMS, AND APPARATUSES FOR IMAGE SEGMENTATION,” owned by the same party. The contents of that application are incorporated herein by reference in their entirety.

[0028] In step 304, method 300 may include generating a plurality of transducer layouts for applying a TT field to a subject based on a 3D model of the subject. The transducer layout may define one or more relative positions to the subject for positioning the transducers. In some embodiments, the plurality of transducer layouts may include four positions on the subject for positioning each of the four transducers, such as the subject's head or torso. In some embodiments, the plurality of transducer layouts may include two positions on the subject for positioning each of the two transducer arrays, such as the subject's head or torso. In some embodiments, each transducer may include one or more electrode elements. The electrode elements may be of any suitable type or material. For example, at least one electrode element may include a ceramic dielectric layer, a polymer film, and / or a combination and / or a combination thereof. The generation of the plurality of transducer layouts may be performed after receiving a selection by the user from a user interface to initiate the generation. In some embodiments, the generation of multiple transducer layouts in step 304 can be carried out using the technology of the commonly owned U.S. Patent Application Publication No. 2021 / 0201572, titled "METHODS, SYSTEMS, AND APPARATUSES FOR IMAGE SEGMENTATION." The contents of that application are incorporated herein by reference in their entirety.

[0029] In step 306, method 300 may include selecting at least two of the transducer layouts as recommended transducer layouts to present to the user. In some embodiments, at least one of the recommended transducer layouts may have similar elements including the highest dose of TT field delivered to the ROI, the highest dose of TT field delivered to the tumor progression area, and / or combinations and / or multiples thereof. In some embodiments, at least one of the recommended transducer layouts may be a position-shifted or rotated arrangement compared to the transducer layout that delivers the highest dose of TT field to the ROI. In some embodiments, at least three of the recommended transducer layouts may deliver three highest doses of TT field to the ROI.

[0030] In step 308, method 300 may include presenting recommended transducer array layouts. For example, method 300 may include presenting at least four recommended transducer layouts, but in other embodiments, more or fewer transducer layouts may be presented. In some embodiments, presenting recommended transducer layouts may include presenting information about the recommended transducer layouts via a user interface. The information may include one or more similar elements, including: the dose of the TT field delivered to the ROI for each of the recommended transducer layouts; slices of medical images with the TT field dose superimposed for at least one recommended transducer layout; a two-dimensional graph comparing the percentage volume of the ROI with the percentage dose of the TT field for at least one recommended transducer layout; images of subjects depicting the positions of electrode elements for at least one recommended transducer layout; a two-dimensional graph depicting the cumulative dose of the TT field across the ROI for at least one recommended transducer layout; a two-dimensional graph depicting the dose of the TT field across the ROI for at least one recommended transducer layout; the percentage of overlap between electrode elements of two recommended transducer layouts; the percentage of overlap between bonding portions of two recommended transducer layouts; and / or combinations thereof.

[0031] In step 310, method 300 may include receiving a user selection of at least one recommended transducer array layout. In some embodiments, the user may select a primary transducer layout and an alternate transducer layout. In making the selection, the user may accept the primary layout as the first layout, then consider and evaluate the alternate layout, and select the alternate layout as the second layout. For example, the user may select one transducer layout (e.g., primary) to use during the first period. After the first period, the user may select another transducer layout (e.g., alternate) to use during the second period.

[0032] In step 312, method 300 may include providing a report relating to at least one selected recommended transducer layout. In some embodiments, the report may show the positions of the transducers in the selected recommended transducer arrangement on the subject from multiple viewpoints. In some embodiments, the report may present the dosage of TT field applied to the subject. It should be understood that different reports may be provided, for example, depending on the intended target of the report (e.g., a first report type for the subject, a second report type for inclusion in the subject's medical record).

[0033] Referring to Figure 4, Figure 4 shows an example of a user interface for an application for manually segmenting a medical image of a subject. Specifically, Figure 4 shows multiple user-selectable options 402 (e.g., user-selectable icons) for manually segmenting slices displayed in a dropdown menu. Examples of user-selectable options 402 may include, for example, a user-selectable icon for automatically filling a region (e.g., a polybrush option), a user-selectable icon for selecting a region without automatic filling (e.g., a paintbrush option), a user-selectable icon for erasing a segmentation (e.g., an erase option), a user-selectable icon for assigning a tissue type (e.g., an assignment option), a user-selectable icon for extending the boundary of a region (e.g., an enlarge and margin option), a user-selectable icon for cleaning up a segmented slice, a user-selectable icon for splitting a segmentation, and / or combinations and / or multiples thereof. In some embodiments, the user-selectable options 402 provide a set of tools for the user to select. The tools may be used to segment abnormal tissue. For example, a polybrush or paintbrush may be used to draw the outline of an abnormal tissue area. Abnormal tissue can be any undesirable tissue type, including tumors, necrotic tissue, previous surgical sites (e.g., excision cavities), and / or combinations thereof, and / or similar elements.

[0034] Figure 5 shows an example of a user interface for an application that automatically generates ROIs in a subject's medical image. As shown in the example in Figure 5, the user may start the automatic generation of ROIs by clicking the "Automatic ROI Generation" button at 502, which displays the "Automatic ROI Generation" window. The window may display at 504 a user-adjustable value for the PBZ margin to add to and / or extend the segmented region in the medical image, thereby obtaining an extended segmented region in the medical image. The window may display at 506 user-selectable tissue types for adding the PBZ margin to the segmented region. Examples of user-selectable tissue types may include resection cavity, necrotic center, or augmented tumor. The window may display at 508 instructions showing how the margins are joined with different tissue types. For example, an augmented tumor may be assigned to a GTV, and that GTV may be assigned to a CTV in the ROI. For example, a PBZ may be assigned to a CTV in the ROI. After making these selections, the user may click the button at 510 to accept these parameters and start the automatic generation of ROIs using the selected parameters.

[0035] Figure 6 shows an example of a user interface for a warning regarding the automatic generation of ROIs in a subject's medical images. Figure 6 shows an example of a warning being presented to the user when the minimum number of voxels in the medical image for segmentation is not met. For example, the warning in Figure 6 may indicate that CTV and PBZ have been added to complete the ROI generation, and that CTV will be the primary ROI.

[0036] Figures 7A to 7C show examples of medical images at various stages of a subject with an automatically generated ROI within the medical image according to method 200 in Figure 2. In Figure 7A, step 202 generates two segment regions within the medical image: a segmented region 712 of the enhanced tumor and a segmented region 714 of the resection cavity. In Figure 7B, step 206 adds a PBZ margin 716 (e.g., 3 mm) to the segmented region 712 of the enhanced tumor and also to the segmented region 714 of the resection cavity. This generates a contrast-enhanced segmented region. In Figure 7C, step 208 combines the two contrast-enhanced segmented regions to obtain an ROI 718.

[0037] Figure 8 shows an exemplary system 800 for applying an alternating current field (e.g., a TT field) to a subject's body. The system can be used to treat a target area of ​​the subject's body with the alternating current field. For example, the target area may be in the subject's brain, and the alternating current field may be delivered to the subject's body via two pairs of transducer arrays (e.g., four transducers 900 in Figure 9) positioned above the subject's head. In one example, the target area is in the subject's torso, and the alternating current field is delivered to the subject's body via two pairs of transducer arrays positioned in at least one of the subject's chest, abdomen, or one or both thighs. Other arrangements of transducer arrays on the subject's body are also possible.

[0038] An exemplary apparatus 800 has four transducers (or “transducer arrays”) 800A–D. Each transducer 800A–D may include substantially flat electrode elements 802A–D, which are arranged on substrates 804A–D and electrically and physically connected (e.g., via conductive wiring 806A–D). The substrates 804A–D may include, for example, cloth, foam, flexible plastic, and / or conductive medical gel. Two transducers (e.g., 800A and 800D) may be a first pair of transducers configured to apply an alternating electric field to a target area of ​​the subject's body. The other two transducers (e.g., 800B and 800C) may be a second pair of transducers similarly configured to apply an alternating electric field to a target area.

[0039] Transducers 800A-D may be coupled to an AC voltage generator 820, and the system may further include a controller 810 communicatively coupled to the AC voltage generator 820. The controller 810 may include a computer having one or more processors 824 and a memory 826 accessible by one or more processors. The memory 826 may store instructions that, when executed by one or more processors, cause the computer to control the AC voltage generator 820 to induce an alternating electric field between the transducer pair 800A-D according to one or more voltage waveforms, and / or one or more methods disclosed herein. The controller 810 may monitor the operation performed by the AC voltage generator 820 (e.g., via a processor 824). One or more sensors 828 may be coupled to the controller 810 to provide the controller 810 with measurements or other information.

[0040] Electrode elements 802A to D can be capacitively coupled. In one example, electrode elements 802A to D are ceramic electrode elements coupled to each other via conductive wiring 806A to D. The ceramic electrode elements may be circular or non-circular when viewed from a direction perpendicular to their surface. In other embodiments, the array of electrode elements may not be capacitively coupled, and the dielectric material (such as a ceramic or high-dielectric polymer layer) associated with the electrode elements may not be present.

[0041] The structure of transducers 800A-D can take various forms. The transducer may be attached to the subject's body, or attached to or incorporated into clothing covering the subject's body. The transducer may include suitable materials for attaching the transducer to the subject's body. Suitable materials may include, for example, cloth, foam, flexible plastic, and / or conductive medical gel. The transducer may be conductive or non-conductive.

[0042] A transducer may include any desired number of electrode elements (e.g., one or more electrode elements). For example, a transducer may include one, two, three, four, five, six, seven, eight, nine, ten, or more electrode elements (e.g., twenty electrode elements). Electrode elements may be of various shapes, sizes, and materials. Any structure for implementing a transducer (or electric field generator) used in conjunction with embodiments of the present invention may be used, insofar as they are capable of (a) delivering an AC field to the subject's body and (b) being positioned at locations specified herein. Transducers may be conductive or nonconductive. In some embodiments, an AC signal may be capacitively coupled to the subject's body. In some embodiments, at least one electrode element of the first, second, third, or fourth transducer may include at least one ceramic disk adapted to generate an AC electric field. In some embodiments, at least one electrode element of the first, second, third, or fourth transducer may include a polymer film adapted to generate an alternating electric field.

[0043] Figure 10 shows an exemplary computer device for use in embodiments of the present invention. For example, device 1000 may be a computer for performing certain inventive techniques disclosed herein, such as selecting the position of a transducer for delivering a TT field to a subject. As an example, method 100 in Figure 1 may be performed by a computer such as device 1000. As another example, method 200 in Figure 2 may be performed by a computer such as device 1000. This computer may be the same computer used to perform method 100 in Figure 1, or a different computer. As yet another example, method 300 in Figure 3 may also be performed by a computer such as device 1000. This device 1000 may be the same computer used to perform methods 100 and 200, or a different computer. In some embodiments, the controller 810 shown in Figure 8 may be implemented by device 1000. Device 1000 may include one or more processors 1002, memory 1003, one or more input devices (not shown), and one or more output devices 1005.

[0044] In some embodiments, based on input 1001, one or more processors 1002 may generate control signals for controlling a voltage generator. For example, input 1001 is a user input. For example, input 1001 may be an input from another computer communicating with device 1000. Input 1001 may be received in combination with one or more input devices (not shown) of device 1000.

[0045] Memory 1003 is accessible by one or more processors 1002 (for example, via link 1004), and one or more processors 1002 may read information from or write information to memory 1003. Memory 1003 may store instructions that, when executed by one or more processors 1002, implement one or more embodiments described herein.

[0046] One or more output devices 1005 may provide information about the operation of the present invention, such as the selection of the transducer array, the voltage generated, and other operational information. Output devices 1005 may provide visualization data according to some embodiments described herein.

[0047] The device 1000 may be a device for planning a treatment for administering a tumor treatment to a subject, and the device includes one or more processors (such as one or more processors 1002) and a memory accessible by one or more processors (such as memory 1003) that, when executed by one or more processors, stores instructions causing the device to perform one or more methods described herein.

[0048] Memory 1004 may be a non-temporary processor-readable medium containing a set of instructions therein for administering a tumor treatment to a subject, the instructions, when executed by a processor (such as processor 1002), cause the processor to perform one or more of the methods described herein. Exemplary Embodiments

[0049] The present invention includes other exemplary embodiments ("Embodiments") as follows:

[0050] (Embodiment 1) A computer implementation method for a treatment plan for administering a tumor treatment field to a subject, the method comprising: presenting a slice of a medical image of the subject including voxels on a display; determining the segmentation of the minimum number of voxels of at least one tissue type in the slice of the medical image; receiving a user selection for automatically generating a region of interest (ROI) in the medical image for applying the tumor treatment field to the subject; and automatically generating the region of interest in the medical image for applying the tumor treatment field to the subject based on the segmentation of the minimum number of voxels of at least one tissue type in the slice of the medical image.

[0051] (Embodiment 2) The computer implementation method according to Embodiment 1, wherein determining the segmentation of the minimum number of voxels of at least one tissue type in the slice of the medical image includes determining the segmentation of the minimum number of voxels of a tumor in the medical image.

[0052] (Embodiment 3) The computer implementation method according to Embodiment 1, wherein determining the segmentation of the minimum number of voxels for at least one tissue type in the slice of the medical image includes determining the segmentation of the minimum number of voxels for macroscopic tumor volume in the medical image.

[0053] (Embodiment 4) The computer implementation method according to Embodiment 1, wherein determining the segmentation of the minimum voxel number of at least one tissue type in the slice of the medical image includes at least one of: determining the segmentation of the minimum voxel number of an excision cavity in the medical image; determining the segmentation of the minimum voxel number of a necrotic area in the medical image; and determining the segmentation of the minimum voxel number of an augmented tumor in the medical image.

[0054] (Embodiment 5) The computer implementation method according to Embodiment 1, wherein the region of interest in the medical image for applying a tumor treatment field to the subject includes a clinical tumor volume in the medical image for applying a tumor treatment field to the subject.

[0055] (Embodiment 6) The computer implementation method according to Embodiment 1, wherein the automatic generation of the region of interest in the medical image for applying a tumor treatment field to the subject includes adding a proximal boundary region (PBZ) to the macroscopic tumor volume (GTV) in the medical image to obtain a clinical tumor volume (CTV) for applying a tumor treatment field to the subject.

[0056] (Embodiment 7) A computer implementation method according to Embodiment 1 for automatically generating the region of interest in the medical image for applying a tumor treatment field to the subject, comprising: determining a segmented region of a tissue type in the medical image based on the segmentation of at least one tissue type in the slice in the medical image based on the minimum number of voxels of that tissue type; determining a proximal boundary region (PBZ) to the segmented region of the medical image; and adding the PBZ to the segmented region of the medical image to obtain the region of interest in the medical image for applying a tumor treatment field to the subject.

[0057] (Embodiment 8) The computer implementation method according to Embodiment 7, wherein the segmented region is a tumor, macroscopic tumor volume, resection cavity, necrotic area, enhanced tumor, or non-enhanced tumor.

[0058] (Embodiment 9) A computer implementation method according to Embodiment 1 for automatically generating the region of interest in the medical image for applying a tumor treatment field to the subject, comprising: determining at least two segmented regions of different tissue types in the medical image based on the segmentation based on the minimum number of voxels of at least one tissue type in the slice in the medical image; determining a margin for each segmented region of the medical image; adding the margin to each segmented region of the medical image to obtain an expanded segmented region; and combining the expanded segmented regions to obtain the region of interest in the medical image for applying a tumor treatment field to the subject.

[0059] (Embodiment 10) The computer implementation method according to Embodiment 9, wherein at least two segmented regions include at least two of a tumor, macroscopic tumor volume, resection cavity, necrotic area, enhanced tumor, or non-enhanced tumor.

[0060] (Embodiment 10A) The computer implementation method according to Embodiment 9, wherein the region of interest includes the overlapping portion and the non-overlapping portion of the extended segmentation region.

[0061] (Embodiment 10B) The computer implementation method according to Embodiment 9, wherein the PBZ is the same value for each segmented region in the medical image.

[0062] (Embodiment 10C) The computer implementation method according to Embodiment 9, wherein the PBZ is a different value for each segmented region in the medical image.

[0063] (Embodiment 10D) The computer implementation method according to Embodiment 9, wherein the PBZ is a user-selectable value for each segmented region in the medical image.

[0064] (Embodiment 11) The computer implementation method according to Embodiment 1, wherein the automatic generation of the region of interest in the medical image for applying a tumor treatment field to the subject includes determining a proximal boundary region (PBZ) for extending the segmented region in the medical image and obtaining an extended segmented region in the medical image.

[0065] (Embodiment 12) The computer mounting method according to Embodiment 15, wherein the PBZ with respect to the segmented region of the medical image is 3 mm.

[0066] (Embodiment 12A) The computer mounting method according to Embodiment 15, wherein the PBZ with respect to the segmented region of the medical image is between 1 mm and 15 mm, or between 1 mm and 20 mm.

[0067] (Embodiment 13) The computer implementation method according to Embodiment 1, wherein the automatic generation of the region of interest in the medical image for applying a tumor treatment field to the subject includes providing a user-adjustable value for display of the proximal boundary region (PBZ) for expanding the segmented region in the medical image to obtain an expanded segmented region in the image.

[0068] (Embodiment 14) The computer implementation method according to Embodiment 1, wherein the minimum number of voxels in the medical image for segmentation is 10 voxels.

[0069] (Embodiment 14A) The computer implementation method according to Embodiment 1, wherein the minimum number of voxels in the medical image for segmentation is between 5 and 25 voxels.

[0070] (Embodiment 15) The computer implementation method according to Embodiment 1, wherein a warning is presented to the user if the minimum number of voxels in the medical image for segmentation is not met.

[0071] (Embodiment 16) The computer implementation method according to Embodiment 1, further comprising generating a plurality of transducer layouts for applying a tumor treatment field to the subject based on the automatically generated region of interest in the medical image.

[0072] (Embodiment 17) A computer implementation method according to Embodiment 1, further comprising: creating a three-dimensional model of the subject including the automatically generated region of interest based on the medical image; generating a plurality of transducer layouts for applying a tumor treatment field to the subject based on the three-dimensional model of the subject; selecting at least two of the plurality of transducer layouts as recommended transducer layouts; presenting the recommended transducer layouts; receiving a user selection of at least one recommended transducer layout; and providing a report relating to the at least one selected recommended transducer layout.

[0073] (Embodiment 18) The computer implementation method according to Embodiment 1, wherein the medical image includes at least one computed tomography (CT) medical image, magnetic resonance imaging (MRI) medical image, or positron emission tomography (PET) medical image.

[0074] (Embodiment 18A) The computer implementation method according to Embodiment 1, wherein the medical image includes the torso of the subject.

[0075] (Embodiment 18B) The computer implementation method according to Embodiment 1, wherein the medical image includes the head of the subject.

[0076] (Embodiment 19A) A non-temporary processor-readable medium comprising a series of instructions for administering a tumor treatment field to a subject, wherein, when the instructions are executed by the processor, the processor is instructed to perform a method comprising: presenting a slice of a medical image of the subject, including voxels, on a display; determining a minimum voxel number segmentation of at least one tissue type in the slice of the medical image; receiving a user selection for automatically generating a region of interest (ROI) in the medical image for applying a tumor treatment field to the subject; and automatically generating the region of interest in the medical image for applying a tumor treatment field to the subject based on the minimum voxel number segmentation of at least one tissue type in the slice of the medical image.

[0077] (Embodiment 20) Apparatus for administering a tumor treatment area to a subject, the apparatus comprising one or more processors and a memory accessible by the one or more processors, the memory storing instructions causing the apparatus to perform a method, when executed by the one or more processors, that includes: presenting a slice of a medical image of the subject containing voxels on a display; determining the minimum voxel number segmentation of at least one tissue type in the slice of the medical image; receiving a user selection for automatically generating a region of interest (ROI) in the medical image for applying the tumor treatment area to the subject; and automatically generating the region of interest in the medical image for applying the tumor treatment area to the subject based on the minimum voxel number segmentation of at least one tissue type in the slice of the medical image.

[0078] (Embodiment 21) A method, machine, product, and / or system substantially identical to those illustrated and described.

[0079] Optionally, in each embodiment described herein, the voltage generating component supplies the transducer with an AC waveform suitable for delivering TT field therapy to the subject's body at a frequency in the range of about 50 kHz to about 1 MHz.

[0080] Embodiments described in any heading or portion of this disclosure may be combined with embodiments described in the same heading or other portions of this disclosure, unless otherwise stated herein or unless the context clearly contradicts the description. For example, an embodiment described in dependent claim form to a given embodiment (e.g., a given embodiment described in independent claim form) may be combined with other embodiments (described in independent or dependent claim form).

[0081] Numerous modifications, alterations, and changes are possible to the embodiments described without departing from the scope of the invention as defined in the claims. The invention is not limited to the embodiments described and is intended to encompass the entire scope as defined by the following claims and their equivalents.

Claims

1. A computer implementation method for a treatment plan for administering a tumor treatment drug to a subject, wherein the method is: Presenting a slice of the subject's medical image, including voxels, on a display, To determine the segmentation of the minimum number of voxels for at least one tissue type in the slice of the medical image, Receiving user selections for automatically generating a region of interest (ROI) within the medical image for applying a tumor treatment area to the subject, A method comprising: automatically generating the region of interest in the medical image for applying a tumor treatment field to the subject, based on the segmentation of the minimum number of voxels of at least one tissue type in the slice of the medical image.

2. Determining the segmentation of the minimum number of voxels for at least one tissue type in the slice of the medical image is: The method according to claim 1, comprising determining the segmentation of the tumor by the minimum number of voxels in the medical image.

3. Determining the segmentation of the minimum number of voxels for at least one tissue type in the slice of the medical image is: The method according to claim 1, comprising determining the segmentation of the minimum number of voxels for macroscopic tumor volume in the medical image.

4. Determining the segmentation of the minimum number of voxels for at least one tissue type in the slice of the medical image is: To determine the segmentation of the resection cavity in the aforementioned medical image by the minimum number of voxels, To determine the segmentation of the necrotic region in the aforementioned medical image by the minimum number of voxels, The method according to claim 1, comprising at least one of determining the segmentation of the augmented tumor in the medical image by the minimum number of voxels.

5. Automatically generating the region of interest within the medical image for applying a tumor treatment field to the subject is: The method according to claim 1, further comprising adding a proximal boundary zone (PBZ) to the gross tumor volume (GTV) in the medical image to obtain a clinical tumor volume (CTV) for applying a tumor treatment field to the subject.

6. Automatically generating the region of interest within the medical image for applying a tumor treatment field to the subject is: Based on the segmentation based on the minimum number of voxels for at least one tissue type in the slice within the medical image, the segmented region of the tissue type in the medical image is determined. Determining the proximal boundary region (PBZ) of the segmented region of the medical image, The method according to claim 1, comprising adding the PBZ to the segmented region of the medical image to obtain the region of interest in the medical image for applying a tumor treatment field to the subject.

7. The method according to claim 6, wherein the segmented region is a tumor, macroscopic tumor volume, resection cavity, necrotic area, enhanced tumor, or non-enhanced tumor.

8. Automatically generating the region of interest within the medical image for applying a tumor treatment field to the subject is: Based on the segmentation based on the minimum number of voxels for at least one tissue type in the slice within the medical image, at least two segmented regions of different tissue types within the medical image are determined. Determining the margins for each segmented region of the aforementioned medical image, The process involves adding the margins to each segmented region of the medical image to obtain an expanded segmented region, The method according to claim 1, comprising: merging the extended segmented regions to obtain the region of interest in the medical image for applying a tumor treatment field to the subject.

9. Automatically generating the region of interest within the medical image for applying a tumor treatment field to the subject is: The method according to claim 1, comprising determining a proximal boundary zone (PBZ) for expanding a segmented region in the medical image, and obtaining an expanded segmented region in the medical image.

10. Automatically generating the region of interest within the medical image for applying a tumor treatment field to the subject is: The method according to claim 1, further comprising providing a user-adjustable value for display of a proximal boundary region (PBZ) for expanding the segmented region in the medical image to obtain an expanded segmented region in the medical image.

11. The method according to claim 1, wherein a warning is presented to the user if the minimum number of voxels in the medical image for segmentation is not met.

12. The method according to claim 1, further comprising generating a plurality of transducer layouts for applying a tumor treatment field to the subject based on the automatically generated region of interest in the medical image.

13. Based on the medical images, a three-dimensional model of the subject including the automatically generated region of interest is created. Based on the three-dimensional model of the subject, a plurality of transducer layouts for applying a tumor treatment field to the subject are generated, Select at least two of the aforementioned multiple transducer layouts as recommended transducer layouts, The recommended transducer layout is presented, To receive a user selection of at least one recommended transducer layout, The method according to claim 1, comprising providing a report relating to at least one selected recommended transducer layout.

14. A non-transient processor-readable medium containing a series of instructions for administering a tumor treatment to a subject, wherein when the instructions are executed by the processor, Presenting a slice of the subject's medical image, including voxels, on a display, To determine the segmentation of the minimum number of voxels for at least one tissue type in the slice of the medical image, Receiving user selections for automatically generating a region of interest (ROI) within the medical image for applying a tumor treatment area to the subject, A non-transient processor-readable medium that causes the processor to perform a method including: automatically generating the region of interest in the medical image for applying a tumor treatment field to the subject based on the segmentation of the minimum number of voxels of at least one tissue type in the slice of the medical image.

15. A device for administering a tumor treatment agent to a subject, the device comprising one or more processors and a memory accessible by the one or more processors, the memory, when executed by the one or more processors, Presenting a slice of the subject's medical image, including voxels, on a display, To determine the segmentation of the minimum number of voxels for at least one tissue type in the slice of the medical image, Receiving user selections for automatically generating a region of interest (ROI) within the medical image for applying a tumor treatment area to the subject, A device that stores instructions causing the device to perform a method including: automatically generating a region of interest in the medical image for applying a tumor treatment field to the subject, based on the segmentation of the minimum number of voxels of at least one tissue type in the slice of the medical image.

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