Med-Query: Steerable Parsing of 9-DoF Medical Anatomies with Query Embedding

• URL: http://arxiv.org/abs/2212.02014v3
• Date: Fri, 20 Dec 2024 10:21:14 GMT
• Title: Med-Query: Steerable Parsing of 9-DoF Medical Anatomies with Query Embedding
• Authors: Heng Guo, Jianfeng Zhang, Ke Yan, Le Lu, Minfeng Xu,
• Abstract summary: We propose a steerable, robust, and efficient computing framework for detection, identification, and segmentation of anatomies in CT scans. Considering the complicated shapes, sizes, and orientations of anatomies, we present a nine degrees of freedom (9-DoF) pose estimation solution in full 3D space. We have validated our method on three medical imaging parsing tasks: ribs, spine, and abdominal organs.
• Score: 14.901279446640393
• License:
• Abstract: Automatic parsing of human anatomies at the instance-level from 3D computed tomography (CT) is a prerequisite step for many clinical applications. The presence of pathologies, broken structures or limited field-of-view (FOV) can all make anatomy parsing algorithms vulnerable. In this work, we explore how to leverage and implement the successful detection-then-segmentation paradigm for 3D medical data, and propose a steerable, robust, and efficient computing framework for detection, identification, and segmentation of anatomies in CT scans. Considering the complicated shapes, sizes, and orientations of anatomies, without loss of generality, we present a nine degrees of freedom (9-DoF) pose estimation solution in full 3D space using a novel single-stage, non-hierarchical representation. Our whole framework is executed in a steerable manner where any anatomy of interest can be directly retrieved to further boost inference efficiency. We have validated our method on three medical imaging parsing tasks: ribs, spine, and abdominal organs. For rib parsing, CT scans have been annotated at the rib instance-level for quantitative evaluation, similarly for spine vertebrae and abdominal organs. Extensive experiments on 9-DoF box detection and rib instance segmentation demonstrate the high efficiency and effectiveness of our framework (with the identification rate of 97.0% and the segmentation Dice score of 90.9%), compared favorably against several strong baselines (e.g., CenterNet, FCOS, and nnU-Net). For spine parsing and abdominal multi-organ segmentation, our method achieves competitive results on par with state-of-the-art methods on the public CTSpine1K dataset and FLARE22 competition, respectively. Our annotations, code, and models are available at: https://github.com/alibaba-damo-academy/Med_Query.

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