1. Combined model-based and deep learning-based automated 3D zonal segmentation of the prostate on T2-weighted MR images: clinical evaluation
- Author
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Olivier Rouvière, Paul Cezar Moldovan, Anna Vlachomitrou, Sylvain Gouttard, Benjamin Riche, Alexandra Groth, Mark Rabotnikov, Alain Ruffion, Marc Colombel, Sébastien Crouzet, Juergen Weese, Muriel Rabilloud, Hôpital Edouard Herriot [CHU - HCL], Hospices Civils de Lyon (HCL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, Philips Research [Germany], Philips Research, Centre Hospitalier Lyon Sud [CHU - HCL] (CHLS), Centre pour l'innovation en cancérologie de Lyon (CICLY), Université de Lyon-Université de Lyon, Philips Research Europe - Hamburg, Sector Medical Imaging Systems, Département biostatistiques et modélisation pour la santé et l'environnement [LBBE], Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
Male ,Deep Learning ,[SDV]Life Sciences [q-bio] ,Image Processing, Computer-Assisted ,Prostate ,Humans ,Radiology, Nuclear Medicine and imaging ,General Medicine ,Magnetic Resonance Imaging ,ComputingMilieux_MISCELLANEOUS ,Algorithms ,Pelvis - Abstract
To train and to test for prostate zonal segmentation an existing algorithm already trained for whole-gland segmentation.The algorithm, combining model-based and deep learning-based approaches, was trained for zonal segmentation using the NCI-ISBI-2013 dataset and 70 T2-weighted datasets acquired at an academic centre. Test datasets were randomly selected among examinations performed at this centre on one of two scanners (General Electric, 1.5 T; Philips, 3 T) not used for training. Automated segmentations were corrected by two independent radiologists. When segmentation was initiated outside the prostate, images were cropped and segmentation repeated. Factors influencing the algorithm's mean Dice similarity coefficient (DSC) and its precision were assessed using beta regression.Eighty-two test datasets were selected; one was excluded. In 13/81 datasets, segmentation started outside the prostate, but zonal segmentation was possible after image cropping. Depending on the radiologist chosen as reference, algorithm's median DSCs were 96.4/97.4%, 91.8/93.0% and 79.9/89.6% for whole-gland, central gland and anterior fibromuscular stroma (AFMS) segmentations, respectively. DSCs comparing radiologists' delineations were 95.8%, 93.6% and 81.7%, respectively. For all segmentation tasks, the scanner used for imaging significantly influenced the mean DSC and its precision, and the mean DSC was significantly lower in cases with initial segmentation outside the prostate. For central gland segmentation, the mean DSC was also significantly lower in larger prostates. The radiologist chosen as reference had no significant impact, except for AFMS segmentation.The algorithm performance fell within the range of inter-reader variability but remained significantly impacted by the scanner used for imaging.• Median Dice similarity coefficients obtained by the algorithm fell within human inter-reader variability for the three segmentation tasks (whole gland, central gland, anterior fibromuscular stroma). • The scanner used for imaging significantly impacted the performance of the automated segmentation for the three segmentation tasks. • The performance of the automated segmentation of the anterior fibromuscular stroma was highly variable across patients and showed also high variability across the two radiologists.
- Published
- 2022