期刊
MEDICAL IMAGE ANALYSIS
卷 69, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.media.2020.101954
关键词
Bone tumor segmentation; Deep learning; Convolutional neural network; Multi-view fusion; Relation-context representation learning; Limb salvage
类别
资金
- Technology Project of Shanghai Science and Technology Commission [19441902700, 18441903100]
- Shanghai Municipal Education Commission [20152221]
- National Scientific Foundation of China [91859202, 81771901]
- Clinical Research Plan of SHDC [SHDC2020CR3083B]
The study introduces a deep learning-based method for accurately segmenting pelvic bone tumors in MRI images, which shows superior segmentation accuracy on independent datasets, significant reduction in time consumption, and successful application in improving surgical planning workflow.
Limb salvage surgery of malignant pelvic tumors is the most challenging procedure in musculoskeletal oncology due to the complex anatomy of the pelvic bones and soft tissues. It is crucial to accurately resect the pelvic tumors with appropriate margins in this procedure. However, there is still a lack of efficient and repetitive image planning methods for tumor identification and segmentation in many hos-pitals. In this paper, we present a novel deep learning-based method to accurately segment pelvic bone tumors in MRI. Our method uses a multi-view fusion network to extract pseudo-3D information from two scans in different directions and improves the feature representation by learning a relational context. In this way, it can fully utilize spatial information in thick MRI scans and reduce over-fitting when learning from a small dataset. Our proposed method was evaluated on two independent datasets collected from 90 and 15 patients, respectively. The segmentation accuracy of our method was superior to several com-paring methods and comparable to the expert annotation, while the average time consumed decreased about 100 times from 1820.3 seconds to 19.2 seconds. In addition, we incorporate our method into an efficient workflow to improve the surgical planning process. Our workflow took only 15 minutes to com-plete surgical planning in a phantom study, which is a dramatic acceleration compared with the 2-day time span in a traditional workflow. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
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