Teaching AI the Anatomy Behind the Scan: Addressing Anatomical Flaws in Medical Image Segmentation with Learnable Prior

• URL: http://arxiv.org/abs/2403.18878v2
• Date: Mon, 26 Aug 2024 05:54:21 GMT
• Title: Teaching AI the Anatomy Behind the Scan: Addressing Anatomical Flaws in Medical Image Segmentation with Learnable Prior
• Authors: Young Seok Jeon, Hongfei Yang, Huazhu Fu, Mengling Feng,
• Abstract summary: Key anatomical features, such as the number of organs, their shapes and relative positions, are crucial for building a robust multi-organ segmentation model. We introduce a novel architecture called the Anatomy-Informed Network (AIC-Net) AIC-Net incorporates a learnable input termed "Anatomical Prior", which can be adapted to patient-specific anatomy.
• Score: 34.54360931760496
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Imposing key anatomical features, such as the number of organs, their shapes and relative positions, is crucial for building a robust multi-organ segmentation model. Current attempts to incorporate anatomical features include broadening the effective receptive field (ERF) size with data-intensive modules, or introducing anatomical constraints that scales poorly to multi-organ segmentation. We introduce a novel architecture called the Anatomy-Informed Cascaded Segmentation Network (AIC-Net). AIC-Net incorporates a learnable input termed "Anatomical Prior", which can be adapted to patient-specific anatomy using a differentiable spatial deformation. The deformed prior later guides decoder layers towards more anatomy-informed predictions. We repeat this process at a local patch level to enhance the representation of intricate objects, resulting in a cascaded network structure. AIC-Net is a general method that enhances any existing segmentation models to be more anatomy-aware. We have validated the performance of AIC-Net, with various backbones, on two multi-organ segmentation tasks: abdominal organs and vertebrae. For each respective task, our benchmarks demonstrate improved dice score and Hausdorff distance.

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