Methods and Apparatuses for Visualization of Tumor Segmentation

A computer-implemented method for tumor segmentation, the method comprises

• obtaining two or more segmentation networks trained using a common training set with a plurality of training losses, the training set comprising images of other subjects;
• obtaining image data of a region of interest of a subject's body, wherein the region of interest corresponds to a tumor of the subject's body;
• generating two or more tumor segmentation predictions based on the image data and the two or more segmentation networks;
• calculating a divergence between the two or more tumor segmentation predictions;
• and
• generating a visualization of tumor segmentation uncertainty based on the calculated divergence between the two or more tumor segmentation predictions.

An apparatus comprising:

• one or more processors;
• and memory storing processor-executable instructions that, when executed by the one or more processors, cause the apparatus to:
• obtain a training data set of images of other subjects;
• training two or more segmentation networks using the training set and a unified loss based on a comparison between training outputs of the two or more segmentation networks;
• and
• obtain image data of a region of interest of a subject's body, wherein the region of interest corresponds to a tumor of the subject's body;
• generate two or more segmentation predictions based on the image data and the two or more trained segmentation networks;
• and
• calculate a divergence between the two or more segmentation predictions;
• and
• generate a visualization of tumor segmentation uncertainty based on the segmentation prediction, wherein the visualization of segmentation uncertainty is generated based on a calculated divergence between the two or more segmentation predictions.