2023
Kobayashi, Satoshi; King, Franklin; Hata, Nobuhiko
Automatic segmentation of prostate and extracapsular structures in MRI to predict needle deflection in percutaneous prostate intervention Journal Article
In: INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, vol. 18, no. 3, pp. 449–460, 2023, ISSN: 1861-6410, 1861-6429, (Num Pages: 12 Place: Heidelberg Publisher: Springer Heidelberg Web of Science ID: WOS:000857906200002).
Abstract | Links | BibTeX | Tags: 3-D, 3D U-Net, biopsy, CANCER, Deep learning, guidance, Percutaneous intervention, Prostate, RISK, Segmentation, Ultrasound
@article{kobayashi_automatic_2023,
title = {Automatic segmentation of prostate and extracapsular structures in MRI to predict needle deflection in percutaneous prostate intervention},
author = {Satoshi Kobayashi and Franklin King and Nobuhiko Hata},
doi = {10.1007/s11548-022-02757-2},
issn = {1861-6410, 1861-6429},
year = {2023},
date = {2023-03-01},
journal = {INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY},
volume = {18},
number = {3},
pages = {449–460},
abstract = {Purpose Understanding the three-dimensional anatomy of percutaneous intervention in prostate cancer is essential to avoid complications. Recently, attempts have been made to use machine learning to automate the segmentation of functional structures such as the prostate gland, rectum, and bladder. However, a paucity of material is available to segment extracapsular structures that are known to cause needle deflection during percutaneous interventions. This research aims to explore the feasibility of the automatic segmentation of prostate and extracapsular structures to predict needle deflection. Methods Using pelvic magnetic resonance imagings (MRIs), 3D U-Net was trained and optimized for the prostate and extracapsular structures (bladder, rectum, pubic bone, pelvic diaphragm muscle, bulbospongiosus muscle, bull of the penis, ischiocavernosus muscle, crus of the penis, transverse perineal muscle, obturator internus muscle, and seminal vesicle). The segmentation accuracy was validated by putting intra-procedural MRIs into the 3D U-Net to segment the prostate and extracapsular structures in the image. Then, the segmented structures were used to predict deflected needle path in in-bore MRI-guided biopsy using a model-based approach. Results The 3D U-Net yielded Dice scores to parenchymal organs (0.61-0.83), such as prostate, bladder, rectum, bulb of the penis, crus of the penis, but lower in muscle structures (0.03-0.31), except and obturator internus muscle (0.71). The 3D U-Net showed higher Dice scores for functional structures (p <0.001) and complication-related structures (p <0.001). The segmentation of extracapsular anatomies helped to predict the deflected needle path in MRI-guided prostate interventions of the prostate with the accuracy of 0.9 to 4.9 mm. Conclusion Our segmentation method using 3D U-Net provided an accurate anatomical understanding of the prostate and extracapsular structures. In addition, our method was suitable for segmenting functional and complication-related structures. Finally, 3D images of the prostate and extracapsular structures could simulate the needle pathway to predict needle deflections.},
note = {Num Pages: 12
Place: Heidelberg
Publisher: Springer Heidelberg
Web of Science ID: WOS:000857906200002},
keywords = {3-D, 3D U-Net, biopsy, CANCER, Deep learning, guidance, Percutaneous intervention, Prostate, RISK, Segmentation, Ultrasound},
pubstate = {published},
tppubtype = {article}
}
Purpose Understanding the three-dimensional anatomy of percutaneous intervention in prostate cancer is essential to avoid complications. Recently, attempts have been made to use machine learning to automate the segmentation of functional structures such as the prostate gland, rectum, and bladder. However, a paucity of material is available to segment extracapsular structures that are known to cause needle deflection during percutaneous interventions. This research aims to explore the feasibility of the automatic segmentation of prostate and extracapsular structures to predict needle deflection. Methods Using pelvic magnetic resonance imagings (MRIs), 3D U-Net was trained and optimized for the prostate and extracapsular structures (bladder, rectum, pubic bone, pelvic diaphragm muscle, bulbospongiosus muscle, bull of the penis, ischiocavernosus muscle, crus of the penis, transverse perineal muscle, obturator internus muscle, and seminal vesicle). The segmentation accuracy was validated by putting intra-procedural MRIs into the 3D U-Net to segment the prostate and extracapsular structures in the image. Then, the segmented structures were used to predict deflected needle path in in-bore MRI-guided biopsy using a model-based approach. Results The 3D U-Net yielded Dice scores to parenchymal organs (0.61-0.83), such as prostate, bladder, rectum, bulb of the penis, crus of the penis, but lower in muscle structures (0.03-0.31), except and obturator internus muscle (0.71). The 3D U-Net showed higher Dice scores for functional structures (p <0.001) and complication-related structures (p <0.001). The segmentation of extracapsular anatomies helped to predict the deflected needle path in MRI-guided prostate interventions of the prostate with the accuracy of 0.9 to 4.9 mm. Conclusion Our segmentation method using 3D U-Net provided an accurate anatomical understanding of the prostate and extracapsular structures. In addition, our method was suitable for segmenting functional and complication-related structures. Finally, 3D images of the prostate and extracapsular structures could simulate the needle pathway to predict needle deflections.