Abdominal CT pancreas segmentation using multi-scale convolution with aggregated transformations

Jin Yang; Daniel S. Marcus; Aristeidis Sotiras

doi:10.1117/12.2651702

10 April 2023 Abdominal CT pancreas segmentation using multi-scale convolution with aggregated transformations

Jin Yang, Daniel S. Marcus, Aristeidis Sotiras

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124651R (2023) https://doi.org/10.1117/12.2651702
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Convolutional neural networks (CNNs) are a popular choice for medical image segmentation. However, they may be challenged by the large inter-subject variation in organ shapes and sizes due to CNNs typically employing convolutions with fixed-sized local receptive fields. To address this limitation, we proposed multi-scale aggregated residual convolution (MARC) and iterative multi-scale aggregated residual convolution (iMARC) to capture finer and richer features at various scales. Our goal is to improve single convolutions’ representation capabilities. This is achieved by employing convolutions with varying-sized receptive fields, combining multiple convolutions into a deeper one, and dividing single convolutions into a set of channel-independent sub-convolutions. These implementations result in an increase in their depth, width, and cardinality. The proposed MARC and iMARC can be easily integrated into general CNN architectures and trained end-to-end. To evaluate the improvements of MARC and iMARC on CNNs’ segmentation capabilities, we integrated MARC and iMARC into a standard 2D U-Net architecture for pancreas segmentation on abdominal computed tomography (CT) images. The results showed that our proposed MARC and iMARC enhanced the representation capabilities of single convolutions, resulting in improved segmentation performance with lower computational complexity.

Citation Download Citation

Jin Yang, Daniel S. Marcus, and Aristeidis Sotiras "Abdominal CT pancreas segmentation using multi-scale convolution with aggregated transformations", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124651R (10 April 2023); https://doi.org/10.1117/12.2651702

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