TY - GEN
T1 - Automated Surgical Urethral Length Estimation for Robot-Assisted Radical Prostatectomy
AU - De Nijs, Joris V.
AU - Jaspers, Tim J.M.
AU - Bakker, Aron F.H.A.
AU - Brinkman, Willem M.
AU - De With, Peter H.N.
AU - Van Der Sommen, Fons
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024/5/27
Y1 - 2024/5/27
N2 - Urinary Incontinence (UI) is a major side effect of Robot-Assisted Radical Prostatectomy (RARP). The surgical Urethra Length (SUL) emerges as a crucial predictive factor for postoperative-RARP UI. In response, this study introduces a novel approach for the automated estimation of the SUL from surgical video frames. A dedicated RARP dataset was meticulously curated, placing emphasis on the segmentation of structures crucial for accurate SUL estimation. The dataset contains 282 frames extracted from 114 patients' videos and extra care was taken that all frames of the test set were suited for SUL estimation. Notably, each frame in the test set was annotated by both an expert urologist and a medical research fellow. Eventually, the predictions of the segmentation model are integrated into a heuristic method to determine the SUL. Despite training on a relatively small dataset, we have found a small mean difference between prediction and expert annotation SUL (1.86 ± 3.56 mm). This shows the future potential for automated SUL estimation from video, particularly when sufficient samples per patient are available.
AB - Urinary Incontinence (UI) is a major side effect of Robot-Assisted Radical Prostatectomy (RARP). The surgical Urethra Length (SUL) emerges as a crucial predictive factor for postoperative-RARP UI. In response, this study introduces a novel approach for the automated estimation of the SUL from surgical video frames. A dedicated RARP dataset was meticulously curated, placing emphasis on the segmentation of structures crucial for accurate SUL estimation. The dataset contains 282 frames extracted from 114 patients' videos and extra care was taken that all frames of the test set were suited for SUL estimation. Notably, each frame in the test set was annotated by both an expert urologist and a medical research fellow. Eventually, the predictions of the segmentation model are integrated into a heuristic method to determine the SUL. Despite training on a relatively small dataset, we have found a small mean difference between prediction and expert annotation SUL (1.86 ± 3.56 mm). This shows the future potential for automated SUL estimation from video, particularly when sufficient samples per patient are available.
KW - Deep Learning
KW - Robot-Assisted Radical Prostatectomy
KW - Surgical Urethral Length
UR - https://www.scopus.com/pages/publications/85203392321
U2 - 10.1109/ISBI56570.2024.10635376
DO - 10.1109/ISBI56570.2024.10635376
M3 - Conference contribution
AN - SCOPUS:85203392321
T3 - Proceedings - International Symposium on Biomedical Imaging
BT - IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Conference Proceedings
PB - IEEE Computer Society Press
T2 - 21st IEEE International Symposium on Biomedical Imaging, ISBI 2024
Y2 - 27 May 2024 through 30 May 2024
ER -