Edge detection brush

1 . A computer-implemented method for processing a medical image of a subject, the method comprising:

presenting on a display a slice through the medical image of the subject, wherein the medical image comprises voxels; and

performing automatic edge detection in the slice of the medical image to obtain a segmented slice, wherein the automatic edge detection is based on a user selected voxel in the slice of the medical image, wherein the automatic edge detection is based on a user controllable edge detection brush, wherein not all of the voxels selected by the edge detection brush are designated as an edge,

wherein performing automatic edge detection comprises:

determining the user selected voxel in the slice of the medical image;

determining an edge detection image value based on the user selected voxel in the slice of the medical image;

determining a size of the edge detection brush for use in the automatic edge detection;

determining an edge threshold of the edge detection brush;

determining a range of image values to designate as an edge based on the edge detection image value and the edge threshold;

receiving user selected voxels in the slice based on the edge detection brush interacting with the voxels in the slice and based on the size of the edge detection brush; and

designating user selected voxels in the slice as edge voxels in the slice based on the range of image values to designate as an edge,

wherein determining the edge threshold of the edge detection brush comprises a user selection of the edge threshold, and

wherein the user selection of the edge threshold for the edge detection brush comprises at least one of:

a percentage of pixels designated as edges, or

an image value.

2 . The method of claim 1 , wherein performing automatic edge detection comprises:

determining a radius of the edge detection brush for use in the automatic edge detection.

3 . The method of claim 1 , wherein determining the edge detection image value based on the user selected voxel in the slice of the medical image comprises:

determining a center image value as an image value of the user selected voxel in the slice of the medical image, wherein the user selected voxel is in a center of a user selected location;

determining adjacent image values as image values of voxels adjacent to the user selected voxel;

calculating an average image value based on the center image value and the adjacent image values; and

assigning the edge detection image value as the average image value.

4 . The method of claim 1 , wherein determining the edge detection image value based on the user selected voxel in the slice of the medical image comprises:

determining a center image value as an image value of the user selected voxel in the slice of the medical image, wherein the user selected voxel is in a center of a user selected location; and

assigning the edge detection image value as the center image value.

5 . The method of claim 1 , wherein the size of the edge detection brush is a radius of a circular edge detection brush.

6 . The method of claim 1 , wherein the user selection of the edge threshold for the edge detection brush comprises at least the percentage of pixels designated as edges.

7 . The method of claim 1 , wherein the user selection of the edge threshold for the edge detection brush comprises at least the percentage of pixels designated as edges,

wherein the range of image values to designate as an edge comprises a number M of image values in the range, an upper limit UL of the range, and a lower limit LL of the range,

wherein the number M of image values in the range is:

M

=

N

*

(

1

-

P

)

,

where a range of image values for the medical image has a maximum number N of image values, and where P represents the percentage of pixels designated as edges,

wherein the upper limit UL of the range is:

U

⁢

L

=

I

⁢

V

+

0.5

*

M

,

where image value IV is the edge detection image value, and

wherein a lower limit LL of the range is:

L

⁢

L

=

I

⁢

V

-

0.5

*

M

.

8 . The method of claim 1 , wherein the user selection of the edge threshold for the edge detection brush comprises at least the percentage of pixels designated as edges,

wherein the number M of image values in the range is:

M

=

N

*

(

1

-

(

P

*

RL

)

)

,

where a range of image values for the medical image has a maximum number N of image values, where P represents the percentage of pixels designated as edges, and where a range limiter is a percentage RL,

wherein the upper limit UL of the range is:

U

⁢

L

=

I

⁢

V

+

0.5

*

M

,

where image value IV is the edge detection image value, and

wherein a lower limit LL of the range is:

L

⁢

L

=

I

⁢

V

-

0.5

*

M

.

9 . The method of claim 1 , wherein the user selection of the edge threshold for the edge detection brush comprises at least the image value,

wherein an upper limit of the range of image values is a sum of the edge detection image value and the edge threshold, and

wherein a lower limit of the range of image values is a difference between the edge detection image value and the edge threshold.

10 . The method of claim 1 , wherein designating user selected voxels in the slice as edge voxels in the slice comprises:

comparing image values of the user selected voxels to the range of image values.

11 . The method of claim 1 , wherein designating user selected voxels in the slice as edge voxels in the slice comprises, for each user selected voxel in the slice:

designating the user selected voxel as an edge if an image value of the user selected voxel is in the range of image values; and

designating the user selected voxel as not an edge if an image value of the user selected voxel is not in the range of image values.

12 . The method of claim 1 , further comprising:

performing automatic segmentation of a plurality of slices through the medical image to obtain automatically segmented slices through the medical image, the automatic segmentation based on the segmented slice.

13 . The method of claim 12 , wherein the automatically segmented slices are automatically segmented by interpolating between the segmented slice and a second segmented slice.

14 . The method of claim 1 , further comprising:

generating a plurality of transducer layouts for application of tumor treating fields to the subject based on the segmented slice through the medical image.

15 . The method of claim 1 , wherein the segmented slice comprises at least one of a resection cavity or an edema of the subject having an edge detected by the automatic edge detection.

16 . A computer-implemented method for processing a medical image of a subject, the method comprising:

presenting on a display a first slice through a medical image of the subject, wherein the medical image comprises voxels;

performing automatic edge detection in the first slice of the medical image to obtain a first segmented slice, wherein the automatic edge detection is based on a user selected voxel in the first slice of the medical image;

presenting on the display a second slice through the medical image of the subject, wherein the first slice and the second slice are in a same direction and are separated by a plurality of slices through the medical image;

performing automatic edge detection in the second slice of the medical image to obtain a second segmented slice, wherein the automatic edge detection is based on a user selected voxel in the second slice of the medical image;

performing automatic segmentation of the plurality of slices between the first slice and the second slice based on the first segmented slice and the second segmented slice to obtain segmented slices, wherein a segmented medical image comprises the first segmented slice, the second segmented slice, and the segmented slices of the medical image,

wherein performing automatic edge detection in the first slice comprises:

determining the user selected voxel in the first slice of the medical image;

determining an edge detection image value based on the user selected voxel in the first slice of the medical image;

determining a size of the edge detection brush for use in the automatic edge detection;

determining an edge threshold of the edge detection brush;

determining a range of image values to designate as an edge based on the edge detection image value and the edge threshold;

receiving user selected voxels in the first slice based on the edge detection brush interacting with the voxels in the first slice and based on the size of the edge detection brush; and

designating user selected voxels in the first slice as edge voxels in the first slice based on the range of image values to designate as an edge,

wherein determining the edge threshold of the edge detection brush comprises a user selection of the edge threshold, and

wherein the user selection of the edge threshold for the edge detection brush comprises at least one of:

a percentage of pixels designated as edges, or

an image value.

17 . The method of claim 16 , further comprising:

generating a plurality of transducer layouts for application of tumor treating fields to the subject based on the segmented medical image.

18 . The method of claim 16 , further comprising:

defining a region of interest (ROI) in the medical image or in the segmented medical image for application of tumor treating fields to the subject;

creating a three-dimensional model of the subject based on the segmented medical image, the three-dimensional model of the subject including the region of interest;

generating a plurality of transducer layouts for application of tumor treating fields to the subject based on the three-dimensional model of the subject;

selecting at least two of the 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 for the at least one selected recommended transducer layout.

19 . A computer-implemented method for processing a medical image of a subject, the method comprising:

presenting on a display a slice through a medical image of the subject, wherein the medical image comprises voxels;

performing automatic edge detection in the slice of the medical image using a user-controllable edge detection brush to obtain a segmented slice, wherein not all of the voxels selected by the edge detection brush are designated as an edge;

performing automatic segmentation of a plurality of slices through the medical image to obtain automatically segmented slices through the medical image and a segmented medical image, the automatic segmentation based on the segmented slice; and

generating a plurality of transducer layouts for application of tumor treating fields to the subject based on the segmented medical image,

wherein performing automatic edge detection comprises:

determining a user selected voxel in the slice of the medical image;

determining an edge detection image value based on the user selected voxel in the slice of the medical image;

determining a size of the edge detection brush for use in the automatic edge detection;

determining an edge threshold of the edge detection brush;

determining a range of image values to designate as an edge based on the edge detection image value and the edge threshold;

receiving user selected voxels in the slice based on the edge detection brush interacting with the voxels in the slice and based on the size of the edge detection brush; and

designating user selected voxels in the slice as edge voxels in the slice based on the range of image values to designate as an edge,

wherein determining the edge threshold of the edge detection brush comprises a user selection of the edge threshold, and

wherein the user selection of the edge threshold for the edge detection brush comprises at least one of:

a percentage of pixels designated as edges, or

an image value.