An open-source nnU-net algorithm for automatic segmentation of MRI scans in the male pelvis for adaptive radiotherapy

BackgroundAdaptive MRI-guided radiotherapy (MRIgRT) requires accurate and efficient segmentation of organs and targets on MRI scans. Manual segmentation is time-consuming and variable, while deformable image registration (DIR)-based contour propagation may not account for large anatomical changes. Therefore, we developed and evaluated an automatic segmentation method using the nnU-net framework.MethodsThe network was trained on 38 patients (76 scans) with localized prostate cancer and tested on 30 patients (60 scans) with localized prostate, metastatic prostate, or bladder cancer treated at a 1.5 T MRI-linac at our institution. The performance of the network was compared with the current clinical workflow based on DIR. The segmentation accuracy was evaluated using the Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD) metrics.ResultsThe trained network successfully segmented all 600 structures in the test set. High similarity was obtained for most structures, with 90% of the contours having a DSC above 0.9 and 86% having an MSD below 1 mm. The largest discrepancies were found in the sigmoid and colon structures. Stratified analysis on cancer type showed that the best performance was seen in the same type of patients that the model was trained on (localized prostate). Especially in patients with bladder cancer, the performance was lower for the bladder and the surrounding organs. A complete automatic delineation workflow took approximately 1 minute. Compared with contour transfer based on the clinically used DIR algorithm, the nnU-net performed statistically better across all organs, with the most significant gain in using the nnU-net seen for organs subject to more considerable volumetric changes due to variation in the filling of the rectum, bladder, bowel, and sigmoid.ConclusionWe successfully trained and tested a network for automatically segmenting organs and targets for MRIgRT in the male pelvis region. Good test results were seen for the trained nnU-net, with test results outperforming the current clinical practice using DIR-based contour propagation at the 1.5 T MRI-linac. The trained network is sufficiently fast and accurate for clinical use in an online setting for MRIgRT. The model is provided as open-source

MR-guided adaptive radiotherapy for bladder cancer

Radiotherapy has an important role in the curative and palliative treatment settings for bladder cancer. As a target for radiotherapy the bladder presents a number of technical challenges. These include poor tumor visualization and the variability in bladder size and position both between and during treatment delivery. Evidence favors the use of magnetic resonance imaging (MRI) as an important means of tumor visualization and local staging. The availability of hybrid systems incorporating both MRI scanning capabilities with the linear accelerator (MR-Linac) offers opportunity for in-room and real-time MRI scanning with ability of plan adaption at each fraction while the patient is on the treatment couch. This has a number of potential advantages for bladder cancer patients. In this article, we examine the technical challenges of bladder radiotherapy and explore how magnetic resonance (MR) guided radiotherapy (MRgRT) could be leveraged with the aim of improving bladder cancer patient outcomes. However, before routine clinical implementation robust evidence base to establish whether MRgRT translates into improved patient outcomes should be ascertained

An open-source nnU-net algorithm for automatic segmentation of MRI scans in the male pelvis for adaptive radiotherapy

<p>Data related to the article:</p><p>Front. Oncol.</p><p>Sec. Radiation Oncology</p><p>Volume 13 - 2023 | doi: 10.3389/fonc.2023.1285725</p><p> </p><p>An open-source nnU-net algorithm for automatic segmentation of MRI scans in the male pelvis for adaptive radiotherapy</p><p>Ebbe Laugaard Lorenzen 1,2*, Bahar Celik 1, Nis Sarup1, Lars Dysager3, Rasmus Lübeck Christiansen1, Anders Smedegaard Bertelsen1, Uffe Bernchou1,2, Søren Nielsen Agergaard1, Maximilian Lukas Konrad1, Carsten Brink1,2*, Faisal Mahmood1,2, Tine Schytte2,3, Christina Junker Nyborg3</p><p>1 Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, J. B. Winsløws Vej 4, 5000 Odense C, Denmark </p><p>2 Department of Clinical Research, University of Southern Denmark, J.B. Winsløws Vej 19 3., 5000 Odense C, Denmark</p><p>3 Department of Oncology, Odense University Hospital, J. B. Winsløws Vej 4, 5000 Odense C, Denmark</p><p>* Correspondence: </p><p>Ebbe Laugaard Lorenzen</p><p>[email protected]</p><p>Carsten Brink </p><p>[email protected]</p><p> </p&gt