TY - GEN
T1 - Detecting and tracking surgical tools for recognizing phases of the awake brain tumor removal surgery
AU - Fujie, Hiroki
AU - Hirata, Keiju
AU - Horigome, Takahiro
AU - Nagahashi, Hiroshi
AU - Ohya, Jun
AU - Tamura, Manabu
AU - Masamune, Ken
AU - Muragaki, Yoshihiro
N1 - Publisher Copyright:
Copyright © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved
PY - 2019
Y1 - 2019
N2 - In order to realize automatic recognition of surgical processes in surgical brain tumor removal using microscopic camera, we propose a method of detecting and tracking surgical tools by video analysis. The proposed method consists of a detection part and tracking part. In the detection part, object detection is performed for each frame of surgery video, and the category and bounding box are acquired frame by frame. The convolution layer strengthens the robustness using data augmentation (central cropping and random erasing). The tracking part uses SORT, which predicts and updates the acquired bounding box corrected by using Kalman Filter; next, the object ID is assigned to each corrected bounding box using the Hungarian algorithm. The accuracy of our proposed method is very high as follows. As a result of experiments on spatial detection. the mean average precision is 90.58%. the mean accuracy of frame label detection is 96.58%. These results are very promising for surgical phase recognition.
AB - In order to realize automatic recognition of surgical processes in surgical brain tumor removal using microscopic camera, we propose a method of detecting and tracking surgical tools by video analysis. The proposed method consists of a detection part and tracking part. In the detection part, object detection is performed for each frame of surgery video, and the category and bounding box are acquired frame by frame. The convolution layer strengthens the robustness using data augmentation (central cropping and random erasing). The tracking part uses SORT, which predicts and updates the acquired bounding box corrected by using Kalman Filter; next, the object ID is assigned to each corrected bounding box using the Hungarian algorithm. The accuracy of our proposed method is very high as follows. As a result of experiments on spatial detection. the mean average precision is 90.58%. the mean accuracy of frame label detection is 96.58%. These results are very promising for surgical phase recognition.
KW - Awake Brain Tumor Removal Surgery
KW - Computer Vision
KW - Convolutional Neural Network
KW - Data Association
KW - Data Augmentation
KW - Detection
KW - Multiple Object Tracking
UR - http://www.scopus.com/inward/record.url?scp=85064675027&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064675027&partnerID=8YFLogxK
U2 - 10.5220/0007385701900199
DO - 10.5220/0007385701900199
M3 - Conference contribution
AN - SCOPUS:85064675027
T3 - ICPRAM 2019 - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods
SP - 190
EP - 199
BT - ICPRAM 2019 - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods
A2 - De Marsico, Maria
A2 - di Baja, Gabriella Sanniti
A2 - Fred, Ana
PB - SciTePress
T2 - 8th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2019
Y2 - 19 February 2019 through 21 February 2019
ER -