3D medical image segmentation via sequential 2D slice processing

Three-dimensional (3D) medical image segmentation is vital in clinical applications, but faces challenges like computational complexity and spatial context loss in 2D processing. To address these issues, we propose 3DAS2D, which processes 3D images as 2D slices while retaining volumetric data for better accuracy. Our approach integrates modules like Neighbour Attention, Memory Attention, Memory Bank, and Mask Encoder to capture inter-slice dependencies and historical context within a 2D prediction framework. Evaluated on four datasets covering prostate, cardiac, and lung tumour tasks, 3DAS2D performed comparably to 3D methods. This work offers an efficient, adaptable solution bridging 2D efficiency and 3D accuracy, with significant clinical potential.