Publications

@article{bernardes_data-driven_2023,

title = {Data-driven adaptive needle insertion assist for transperineal prostate interventions},

author = {Mariana C. Bernardes and Pedro Moreira and Lisa Mareschal and Clare Tempany and Kemal Tuncali and Nobuhiko Hata and Junichi Tokuda},

doi = {10.1088/1361-6560/accefa},

issn = {0031-9155, 1361-6560},

year  = {2023},

date = {2023-05-01},

journal = {PHYSICS IN MEDICINE AND BIOLOGY},

volume = {68},

number = {10},

pages = {105016},

abstract = {Objective. Clinical outcomes of transperineal prostate interventions, such as biopsy, thermal ablations, and brachytherapy, depend on accurate needle placement for effectiveness. However, the accurate placement of a long needle, typically 150-200 mm in length, is challenging due to needle deviation induced by needle-tissue interaction. While several approaches for needle trajectory correction have been studied, many of them do not translate well to practical applications due to the use of specialized needles not yet approved for clinical use, or to relying on needle-tissue models that need to be tailored to individual patients. Approach. In this paper, we present a robot-assisted collaborative needle insertion method that only requires an actuated passive needle guide and a conventional needle. The method is designed to assist a physician inserting a needle manually through a needle guide. If the needle is deviated from the intended path, actuators shifts the needle radially in order to steer the needle trajectory and compensate for needle deviation adaptively. The needle guide is controlled by a new data-driven algorithm which does not require a priori information about needle or tissue properties. The method was evaluated in experiments with both in vitro and ex vivo phantoms. Main results. The experiments in ex vivo tissue reported a mean final placement error of 0.36 mm with a reduction of 96.25% of placement error when compared to insertions without the use of assistive correction. Significance. Presented results show that the proposed closed-loop formulation can be successfully used to correct needle deflection during collaborative manual insertion with potential to be easily translated into clinical application.},

note = {Num Pages: 14 

Place: Bristol 

Publisher: IoP Publishing Ltd 

Web of Science ID: WOS:000987076600001},

keywords = {biopsy, Brachytherapy, CANCER, Cryoablation, data-driven model, FEASIBILITY, Force, medical robotics, MOTION, needle insertion assist, Robot, TISSUE, transperineal prostate intervention, Ultrasound},

pubstate = {published},

tppubtype = {article}

}

@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}

}