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

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

多孔径变压器用于临床和显微图像的三维分割。
生物结构的三维分割在生物医学成像中至关重要,提供了对结构和功能的重要见解。本文介绍了一种新的生物图像分割方法,该方法将多孔径表示与三维变形金刚(MAT3D)相结合。我们的方法将Transformer网络的全局上下文感知与卷积神经网络(cnn)的局部特征提取能力相结合,为精确描绘复杂生物结构提供了全面的解决方案。首先,我们在ACDC和Synapse多器官分割两个公开的临床数据集上评估了所提出的技术的性能,与已发表的文献相比,Dice得分分别为93.34±0.05和89.73±0.04,参数更少。接下来,我们在包含四种乳腺癌亚型的类器官数据集上评估了我们的技术的性能。该方法的Dice评分为95.12±0.02,PQ评分为97.01±0.01。MAT3D也将参数大幅减少到4000万。代码可在https://github.com/sohaibcs1/MAT3D上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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