Automatic intraprostatic lesion segmentation in multiparametric magnetic resonance images with proposed multiple branch UNet. Issue 12 (24th October 2020)

• Record Type: Journal Article
• Authors: Chen, Yizheng; Xing, Lei; Yu, Lequan; Bagshaw, Hilary P.; Buyyounouski, Mark K.; Han, Bin
• Is Part Of: Medical physics. Volume 47:Issue 12(2020)
• Page Start: 6421
• Page End: 6429
• Publication Date: 2020-10-24
• Subjects: boosting radiotherapy -- deep learning -- intraprostatic lesion segmentation -- multiparametric magnetic resonance imaging -- multiple branch UNet (MB‐UNet); Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.14517
• Languages: English
• ISSNs: 0094-2405

Abstract

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 clinical practice. Methods: Multiparametric magnetic resonance imaging 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 an 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, four in the central zone and one 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 achieved a per case DSC of 0.6333, specificity of 0.9993, sensitivity of 0.7056; and global DSC of 0.7205, specificity of 0.9993, sensitivity of 0.7409. All the three deep learning strategies adopted in this study contributed to the performance promotion of the MB‐UNet. Missing the DWI modality would degrade the segmentation performance more markedly compared with the other two modalities. Conclusions: A deep learning‐based approach with proposed MB‐UNet was developed to automatically segment suspicious lesions in mpMRI images. This study makes it feasible to adopt boosting intraprostatic lesions in clinical practice to achieve better outcomes.