A 2-step deep learning approach to splenic injury detection

Yu Ling Chen*, I. Fang Chung, Chi Tung Cheng, Hou Shian Lin

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

The spleen is the most commonly injured solid organ in blunt abdominal trauma. Physicians can use abdominal CT to understand the location of the injury and determine its severity grade. However, these injuries may go undetected because of the inexperience of junior physicians. In our previous study, we had performed a 2-step object detection based analysis pipeline on abdominal CT images to identify whether the spleens are injured or not, and also reported that localizing the spleen first can enhance the prediction performance. In order to delineate the region of the spleen more precisely, in this study we adopted TotalSegmentator without the need of the training process to segment the contour of the spleen. In addition, we further utilized morphology operations to expand the segmented region, especially useful for the spleen injury cases. Our proposed method improves the baseline which only using TotalSegmentator to get the segmentation of spleen by 6% in terms of accuracy, which confirms its effectiveness.

原文English
主出版物標題2023 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2023
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9798350305791
DOIs
出版狀態Published - 2023
事件2023 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2023 - Penghu, 台灣
持續時間: 26 10月 202329 10月 2023

出版系列

名字2023 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2023

Conference

Conference2023 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2023
國家/地區台灣
城市Penghu
期間26/10/2329/10/23

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