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ORIGINAL RESEARCH article

Front. Med.
Sec. Precision Medicine
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1375851
This article is part of the Research Topic Artificial Intelligence in Imaging, Pathology, and Genetic Analysis of Brain Tumor in the Era of Precision Medicine View all 3 articles

A novel metastatic tumor segmentation method with a new evaluation metric in clinic study

Provisionally accepted
Bing Li Bing Li 1*Qiushi Sun Qiushi Sun 2*Xianjin Fang Xianjin Fang 3*Yang Yang Yang Yang 3*Xiang Li Xiang Li 3,4*
  • 1 The First Hospital of Anhui University of Science and Technology, HuaiNan, China
  • 2 Huashan Hospital, Fudan University, Shanghai, Shanghai Municipality, China
  • 3 School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China
  • 4 School of Safety Science and Engineering, Anhui University of Science and Technology, Huainan, China

The final, formatted version of the article will be published soon.

    Background: Brain metastases are the most common brain malignancies. Automatic detection and segmentation of brain metastases provide significant assistance for radiologists in discovering the location of the lesion and making accurate clinical decisions on brain tumor type for precise treatment. Objectives: However, due to the small size of the brain metastases, existing brain metastases segmentation produces unsatisfactory results and has not been evaluated on clinic datasets. Methodology: In this work, we propose a new metastasis segmentation method DRAU-Net, which integrates a new attention mechanism multi-branch weighted attention module and DResConv module, making the extraction of tumor boundaries more complete. To enhance the evaluation of both the segmentation quality and the number of targets, we propose a novel medical image segmentation evaluation metric: multi-objective segmentation integrity metric, which effectively improves the evaluation results on multiple brain metastases with small size. Results: Experimental results evaluated on the BraTS2023 dataset and collected clinical data show that the proposed method has achieved excellent performance with an average dice coefficient of 0.6858 and multi-objective segmentation integrity metric of 0.5582. Conclusions: Compared with other methods, our proposed method achieved the best performance in the task of segmenting metastatic tumors. .

    Keywords: Brain metastases 1, Precise treatment 2, deep learning 3, Medical image segmentation 4, Multi-Objective Segmentation Integrity Metric 5

    Received: 24 Jan 2024; Accepted: 18 Sep 2024.

    Copyright: © 2024 Li, Sun, Fang, Yang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Bing Li, The First Hospital of Anhui University of Science and Technology, HuaiNan, China
    Qiushi Sun, Huashan Hospital, Fudan University, Shanghai, Shanghai Municipality, China
    Xianjin Fang, School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China
    Yang Yang, School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China
    Xiang Li, School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.