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

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.