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

Siyavash Shabani, Muhammad Sohaib, Sahar A. Mohammed, Bahram Parvin

Abstract:

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.