This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/mp.14517 This article is protected by copyright. All rights reserved Automatic intraprostatic lesion segmentation in multiparametric magnetic resonance images with proposed multiple branch UNet Yizheng Chen, Lei Xing, Lequan Yu, Hilary P. Bagshaw, Mark K Buyyounouski, and Bin Han a) Department of Radiation Oncology, Stanford University, Stanford 94305, USA a) Author to whom correspondence should be addressed. E-mail: hanbin@stanford.edu ABSTRACT Purpose: Contouring intraprostatic lesions is a prerequisite for dose-escalating these lesions in radiotherapy to improve the local cancer control. In this study, a deep learning-based approach was developed for automatic intraprostatic lesion segmentation in multiparametric magnetic resonance imaging (mpMRI) images contributing to the clinical practice. Methods: mpMRI images from 136 patient cases were collected from our institution, and all these cases contained suspicious lesions with Prostate Imaging Reporting and Data System (PI-RADS) score ≥ 4. The contours of the lesion and prostate were manually created on axial T2-weighted (T2W), apparent diffusion coefficient (ADC) and high b-value diffusion-weighted imaging (DWI) images to provide the ground truth data. Then a multiple branch UNet (MB-UNet) was proposed for the segmentation of indistinct target in multi-modality MRI images. An encoder module was designed with three branches for the three MRI modalities separately, to fully extract the high-level features provided by different MRI modalities; an input module was added by using three sub-branches for three consecutive image slices, to consider the contour consistency among different image slices; deep supervision strategy was also integrated into the network to speed up the convergency of the network and improve the performance. The probability maps of the background, normal prostate and lesion were output by the network to generate the segmentation of the lesion, and the performance was evaluated using the Dice similarity coefficient (DSC) as the main metric. Results: A total of 162 lesions were contoured on 652 image slices, with 119 lesions in the peripheral zone, 38 in the transition zone, 4 in the central zone and 1 in the anterior fibromuscular stroma. All prostates were also contoured on 1,264 image slices. As for the segmentation of lesions in the testing set, MB-UNet Accepted Article