  Development of robust general purpose 3D segmentation frameworks using the
latest deep learning techniques is one of the active topics in various
bio-medical domains. In this work, we introduce Temporal Cubic PatchGAN
(TCuP-GAN), a volume-to-volume translational model that marries the concepts of
a generative feature learning framework with Convolutional Long Short-Term
Memory Networks (LSTMs), for the task of 3D segmentation. We demonstrate the
capabilities of our TCuP-GAN on the data from four segmentation challenges
(Adult Glioma, Meningioma, Pediatric Tumors, and Sub-Saharan Africa subset)
featured within the 2023 Brain Tumor Segmentation (BraTS) Challenge and
quantify its performance using LesionWise Dice similarity and $95\%$ Hausdorff
Distance metrics. We demonstrate the successful learning of our framework to
predict robust multi-class segmentation masks across all the challenges. This
benchmarking work serves as a stepping stone for future efforts towards
applying TCuP-GAN on other multi-class tasks such as multi-organelle
segmentation in electron microscopy imaging.
