12 March 2018 Automatic slice segmentation of intraoperative transrectal ultrasound images using convolutional neural networks
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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.
Conference Presentation
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Nooshin Ghavami, Nooshin Ghavami, Yipeng Hu, Yipeng Hu, Ester Bonmati, Ester Bonmati, Rachael Rodell, Rachael Rodell, Eli Gibson, Eli Gibson, Caroline Moore, Caroline Moore, Dean Barratt, Dean Barratt, "Automatic slice segmentation of intraoperative transrectal ultrasound images using convolutional neural networks", Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 1057603 (12 March 2018); doi: 10.1117/12.2293300; https://doi.org/10.1117/12.2293300

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