Abstract
Intra-operative non-enhanced computed tomography (CT) has grown as a prominent tool in interventional surgeries for liver tumor by offering a real-time, more precise imaging guidance over its pre-operative enhanced counterpart as well as intra-operative ultrasound. Though deep learning has demonstrated promising performance in various medical image tasks, the challenges for liver tumor segmentation in the context of intra-operative non-enhanced CT remain due to the scarcity of accurate pixel-level-labeled tumor data and low visibility of tumor without contrast enhancement. In this paper, based on a pre-assumption of consistent relative spatial relation between liver and liver tumor at pre- and intra-surgical stages, we propose a two-step (segmentation - registration) framework to propagate liver tumor segmentation mask from pre-operative enhanced CT to corresponding intra-operative non-enhanced CT via nonrigid registration. A Multi-scale Bridged spatial attention block embedded in the popular “U-shaped” network architecture termed MsBs-Unet is introduced to better accommodate variations of tumor during segmentation. We show that MsBs-Unet uniformly achieves superior performances against other baselines on liver and liver tumor segmentation task in terms of Dice score and HD95 metrics on our in-house data containing a cohort of intra-operative non-enhanced CT scans and Medical Segmentation Decathlon task03 liver tumor dataset respectively. Final registration results were evaluated via tumor landmarks matching on 20 in-house intra-subject paired CT scans for both intensity and feature-based methods. Specifically for the latter, mean target registration error is 3.3 mm, and the overall intra-operative process takes around 48 s per case. The resulting tumor segmentation show gratifying promise of applying our framework for non-enhanced CT-based liver intervention.
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Acknowledgment
The study was supported by National Natural Science Foundation of China (81827805, 82130060, 61821002, 92148205), National Key Research and Development Program (2018YFA0704100, 2018YFA0704104). The project was funded by China Postdoctoral Science Foundation (2021M700772), Zhuhai Industry-University-Research Collaboration Program (ZH22017002210011PWC), Jiangsu Provincial Medical Innovation Center (CXZX202219), Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, and Nanjing Life Health Science and Technology Project (202205045). The funding sources had no role in the writing of the report, or decision to submit the paper for publication.
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Lyu, P. et al. (2023). Registration-Propagated Liver Tumor Segmentation for Non-enhanced CT-Based Interventions. In: Lu, H., et al. Image and Graphics . ICIG 2023. Lecture Notes in Computer Science, vol 14358. Springer, Cham. https://doi.org/10.1007/978-3-031-46314-3_10
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