Bowel segmentation system and methods

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, wherein the patient imaging data comprises one or more voxels;
• 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,
• wherein 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 more voxels;
• binarizing the cluster into one or more groups based on a threshold overlap value;
• and
• generating the bowel segment model based at least on the binarized cluster;
• and
• evaluating the bowel segment model based on a validation data.

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 an overlap threshold value;
• and
• generate a bowel segment model based at least on the binarized cluster.