Systems and methods of feature detection within medical images

A system for colon polyp detection, the system comprising a processor and a memory, the memory storing instructions thereon, that when executed by the processor, cause the processor to:

• receive a set of CT scans, wherein a colon is segmented from the set of CT scans;
• construct a 3D model of the colon from the set of CT scans;
• simulate an inner wall of a region of interest of the colon;
• generate 2D images of the simulated inner wall;
• generate a plurality of feature maps using the 2D images and the 3D model;
• and
• detect, by a convolutional neural network (CNN) model using the plurality of feature maps, a candidate polyp location and size.

A non-transitory computer-readable storage medium, having instructions stored thereon, that, when executed by a processor, cause the processor to:

• receive image data comprising a plurality of CT scan images of at least a portion of a subject;
• segment the CT scan images to identify portions of each image corresponding to the subject's colon;
• analyze axial views of the segmented CT scan images to identify a candidate polyp using a first CNN;
• analyze at least two of axial views, sagittal views, and coronal views CT scan images corresponding to the candidate polyp using a second model to classify the candidate polyp as a polyp or not a polyp;
• and
• generate a user interface that includes the classified candidate polyp.