Multi-Aperture Transformers for 3D (MAT3D) Segmentation of Clinical and Microscopic Images

Muhammad Sohaib, Siyavash Shabani, Sahar A. Mohammed, Garrett Winkelmaier, Bahram Parvin; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 4352-4361

3D segmentation of biological structures is critical in biomedical imaging offering significant insights into structures and functions. This paper introduces a novel segmentation of biological images that couples Multi-Aperture representation with Transformers for 3D (MAT3D) segmentation. Our method integrates the global context-awareness of Transformer networks with the local feature extraction capabilities of Convolutional Neural Networks (CNNs) providing a comprehensive solution for accurately delineating complex biological structures. First we evaluated the performance of the proposed technique on two public clinical datasets of ACDC and Synapse multi-organ segmentation rendering superior Dice scores of 93.34+-0.05 and 89.73+-0.04 respectively with fewer parameters compared to the published literature. Next we assessed the performance of our technique on an organoid dataset comprising four breast cancer subtypes. The proposed method achieved a Dice 95.12+-0.02 and a PQ score of 97.01+-0.01 respectively. MAT3D also significantly reduces the parameters to 40 million. The code is available on https://github.com/sohaibcs1/MAT3D.