BOWEL SEGMENTATION SYSTEM AND METHODS

1 . A method comprising:

receiving patient imaging comprising one or more voxels;

determining a lumen indicator based on the patient imaging;

representing the one or more voxels within a distance of the lumen indicator as one or more feature vectors;

generating, based on the one or more feature vectors, a cluster comprising at least one of the one or move voxels;

binarizing the cluster into one or more groups based on a threshold value; and

generating a bowel segment model based at least on the cluster.

2 . The method of claim 1 , wherein:

the one or more groups comprises at least a positive group and a negative group;

voxels in the positive group are included in the bowel segment model; and

voxels in the negative group are excluded from the bowel segment model.

3 . The method of claim 1 , wherein the bowel segment model represents a segment of abnormal bowel.

4 . The method of claim 1 , wherein the patient imaging comprises at least one of a magnetic resonance image (MRI), an ultrasound, or a computer tomography (CT) image.

5 . The method of claim 4 , wherein the MRI comprises a noncontrast T2-weighted MRI image.

6 . The method of claim 1 , wherein the lumen indicator comprises a three-dimensional centerline of the lumen.

7 . The method of claim 1 , further comprising:

receiving segmentation data; and

evaluating the bowel segment model based on the segmentation data.

8 . The method of claim 7 , wherein evaluating the bowel segment model comprises at least one of comparing the bowel segment model to the segmentation data.

9 . The method of claim 8 , wherein the comparison of the bowel segment model to the segmentation data comprises at least one of a Dice score, a symmetric Hausdorff distance, a mean contour distance, a volume, or a length normalized volume.

10 . The patient of claim 9 , wherein the length normalized volume is based at least in part on a length of the lumen indicator.

11 . The method of claim 7 , wherein the segmentation data comprises manual segmentation data determined by a medical provider.

12 . A method for training an artificial intelligence (AI) model to segment portions of a bowel of a patient comprising:

receiving, by a processing element, patient imaging data associated with a lumen;

receiving, by the processing element, a lumen indicator configured to mark a portion of the lumen;

receiving, by the processing element, a segmentation data based on the lumen indicator, wherein the patient imaging data, the lumen indicator, and the segmentation data comprise training data;

providing the training data to an artificial intelligence algorithm executed by the processing element;

training, by the processing element, the artificial intelligence algorithm using the training data to learn a correlation between the lumen indicator and the segmentation data associated with the lumen within the patient imaging;

determining, by the processing element, a bowel segment model based on the training data; and

evaluating the bowel segment model based on a validation data.

13 . The method of claim 12 , wherein evaluating the bowel segment model comprises at least one of comparing the bowel segment model to the segmentation data.

14 . The method of claim 13 , wherein the segmentation data comprises manual segmentation data determined by a medical provider.

15 . The method of claim 13 , wherein the comparison of the bowel segment model to the segmentation data comprises at least one of a Dice score, a symmetric Hausdorff distance, a mean contour distance, a volume, or a length normalized volume.

16 . The method of claim 12 , wherein the patient imaging comprises one or more voxels, and determining the bowel segment model comprises:

representing the one or more voxels within a distance of the lumen indicator as one or more feature vectors;

generating, based on the one or more feature vectors, a cluster comprising at least one of the one or move voxels;

binarizing the cluster into one or more groups based on a threshold value; and

generating a bowel segment model based at least on the cluster.

17 . A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a processing element, cause the processing element to:

receive patient imaging comprising one or more voxels;

receive a lumen indicator based on the patient imaging;

represent the one or more voxels within a distance of the lumen indicator as one or more feature vectors;

generate, based on the one or more feature vectors, a cluster comprising at least one of the one or move voxels;

binarizing the cluster into one or more groups based on a threshold value; and

generate a bowel segment model based at least on the cluster.

18 . The non-transitory computer-readable storage medium of claim 17 , wherein the bowel segment model represents a segment of abnormal bowel.

19 . The non-transitory computer-readable storage medium of claim 17 , wherein the patient imaging comprises at least one of a magnetic resonance image (MRI), an ultrasound, or a computer tomography (CT) image.

20 . The non-transitory computer-readable storage medium of claim 17 , wherein the lumen indicator comprises a three-dimensional centerline of the lumen.