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Proceedings Paper

Automatic slice segmentation of intraoperative transrectal ultrasound images using convolutional neural networks
Author(s): Nooshin Ghavami; Yipeng Hu; Ester Bonmati; Rachael Rodell; Eli Gibson; Caroline Moore; Dean Barratt
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Paper Abstract

This paper, originally published on 12 March 2018, was replaced with a corrected/revised version on 1 June 2018. If you downloaded the original PDF but are unable to access the revision, please contact SPIE Digital Library Customer Service for assistance. Clinically important targets for ultrasound-guided prostate biopsy and prostate cancer focal therapy can be defined on MRI. However, localizing these targets on transrectal ultrasound (TRUS) remains challenging. Automatic segmentation of the prostate on intraoperative TRUS images is an important step towards automating most MRI-TRUS image registration workflows so that they become more acceptable in clinical practice. In this paper, we propose a deep learning method using convolutional neural networks (CNNs) for automatic prostate segmentation in 2D TRUS slices and 3D TRUS volumes. The method was evaluated on a clinical cohort of 110 patients who underwent TRUS-guided targeted biopsy. Segmentation accuracy was measured by comparison to manual prostate segmentation in 2D on 4055 TRUS images and in 3D on the corresponding 110 volumes, in a 10-fold patient-level cross validation. The proposed method achieved a mean 2D Dice score coefficient (DSC) of 0.91±0.12 and a mean absolute boundary segmentation error of 1.23±1.46mm. Dice scores (0.91±0.04) were also calculated for 3D volumes on the patient level. These suggest a promising approach to aid a wide range of TRUS-guided prostate cancer procedures needing multimodality data fusion.

Paper Details

Date Published: 12 March 2018
PDF: 7 pages
Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 1057603 (12 March 2018); doi: 10.1117/12.2293300
Show Author Affiliations
Nooshin Ghavami, Univ. College London (United Kingdom)
Yipeng Hu, Univ. College London (United Kingdom)
Ester Bonmati, Univ. College London (United Kingdom)
Rachael Rodell, Univ. College London (United Kingdom)
Eli Gibson, Univ. College London (United Kingdom)
Caroline Moore, Univ. College London (United Kingdom)
Univ. College London Hospitals NHS Foundation Trust (United Kingdom)
Dean Barratt, Univ. College London (United Kingdom)

Published in SPIE Proceedings Vol. 10576:
Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Robert J. Webster III, Editor(s)

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