TY - GEN
T1 - A 2-step deep learning approach to splenic injury detection
AU - Chen, Yu Ling
AU - Chung, I. Fang
AU - Cheng, Chi Tung
AU - Lin, Hou Shian
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - computed tomography (CT)
KW - deep learning (DL)
KW - splenic injury detection
UR - http://www.scopus.com/inward/record.url?scp=85179585906&partnerID=8YFLogxK
U2 - 10.1109/iFUZZY60076.2023.10324079
DO - 10.1109/iFUZZY60076.2023.10324079
M3 - Conference contribution
AN - SCOPUS:85179585906
T3 - 2023 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2023
BT - 2023 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2023
Y2 - 26 October 2023 through 29 October 2023
ER -