CAN3D: Fast 3D Medical Image Segmentation via Compact Context Aggregation

no code implementations • 12 Sep 2021 • Wei Dai, Boyeong Woo, Siyu Liu, Matthew Marques, Craig B. Engstrom, Peter B. Greer, Stuart Crozier, Jason A. Dowling, Shekhar S. Chandra

Direct automatic segmentation of objects from 3D medical imaging, such as magnetic resonance (MR) imaging, is challenging as it often involves accurately identifying a number of individual objects with complex geometries within a large volume under investigation.

Image Segmentation Medical Image Segmentation +1

Manipulating Medical Image Translation with Manifold Disentanglement

no code implementations • 27 Nov 2020 • Siyu Liu, Jason A. Dowling, Craig Engstrom, Peter B. Greer, Stuart Crozier, Shekhar S. Chandra

In this work, we propose Manifold Disentanglement Generative Adversarial Network (MDGAN), a novel image translation framework that explicitly models these two types of features.

Disentanglement Generative Adversarial Network +1