Abstract #2423

Multi-Task Learning based 3-Dimensional Striatal Segmentation of MRI – PET and fMRI Objective Assessment

Mario Serrano-Sosa¹, Jared Van Snellenberg², Jiayan Meng², Jacob Luceno², Karl Spuhler³, Jodi Weinstein², Anissa Abi-Dargham², Mark Slifstein², and Chuan Huang^2,4

¹Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States, ²Psychiatry, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, United States, ³Radiation Oncology, NYU Langone, New York, NY, United States, ⁴Radiology, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, United States

Segmenting striatal subregions can be difficult; wherein atlas-based approaches have been shown to be less reliable in patient populations and have problems segmenting smaller striatal ROI’s. We developed a Multi-Task Learning model to segment multiple 3D striatal subregions using a Convolutional Neural Network and compared it to the Clinical Imaging Center atlas (CIC). Dice Score Coefficient and multi-modal objective assessment (PET and fMRI) were conducted to evaluate the reliability of MTL-generated segmentations compared to atlas-based. Overall, MTL-generated segmentations were more comparable to manual than CIC across all ROI’s and analyses. Thus, we show MTL method provides reliable striatal subregion segmentations.

This abstract and the presentation materials are available to members only; a login is required.

Join Here