Systems and methods for deep-learning-based segmentation of composite images

A system for automatically processing 3D images to identify 3D volumes corresponding to a particular target tissue region within a subject, the system comprising:

• a processor of a computing device;
• and
• a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to:
• (a) receive a 3D anatomical image of the subject obtained using an anatomical imaging modality, wherein the 3D anatomical image comprises a graphical representation of tissue within the subject;
• (b) receive a 3D functional image of the subject obtained using a functional imaging modality;
• (c) automatically generate using a machine learning module, a 3D segmentation mask that identifies, within the 3D anatomical image and/or the 3D functional image, a 3D volume of interest (VOI) corresponding to the target tissue region,
• wherein the machine learning module:
• (i) receives at least two channels of input, said input channels comprising a first input channel corresponding to at least a portion of the 3D anatomical image and a second input channel corresponding to at least a portion of the 3D functional image and (ii) utilizes both the portion of the 3D anatomical image and the portion of the 3D functional image to perform image segmentation;
• and
• (d) store and/or provide for display and/or further processing, the 3D segmentation mask.