Editorial for “Deep Learning Whole‐Gland and Zonal Prostate Segmentation on a Public MRI Dataset”

Fast and accurate segmentation of organ volumes and their structural subdivisions remains one of the greatest challenges in image analysis and a major barrier to the adoption of quantitative imaging biomarkers into routine clinical practice. Recent advances in machine learning and, in particular, deep learning hold great potential to automate organ delineation and region of interest definition, potentially matching skilled experts. One of the most promising applications for deep learning-driven segmentations is the accurate outlining of the prostate gland, its zones, and focal lesions on MRI studies. The increased incidence of prostate cancer, robust clinical guidelines, and sophisticated treatment options combined with the maturity of the field of prostate MRI and technological advances in computational power have created the perfect environment for the development and clinical translation of deep learning solutions for the quantification of intraprostatic tumor burden. In this issue of JMRI, Cuocolo et al report on the performance of three machine learning networks—U-net, Efficient Neural Network (ENet), and Efficient Residual Factorized ConvNet (ERFNet)—for whole prostate, transition, and peripheral zone segmentation by assessing outlining accuracy, training speed, and hardware requirements. The authors use data from a public repository of T2-weighted MR images split into training and test sets together with manual ground truth segmentations of the 1) whole gland, 2) central zone, anterior stroma, and transition zone, and 3) peripheral zone by four radiologists. The paper describes in detail the data preparation and training steps used by the machine learning algorithms and the rationale behind the choice of network evaluation metrics, including those related to computational complexity and performance (number of parameters, data size on disk, training times, and inference times). The value of the presented results rests with the paper’s focus on clinical utility and ease of implementation. The authors show that ENet converges faster than both ERFNet and U-net and with an order of magnitude fewer parameters than U-net. The faster, more compact, and training-time efficient characteristics of ENet make it feasible to implement on central processing unit (CPU) systems while preserving similar segmentation accuracy when compared to the other two models and the published literature. In addition, generating automatic whole prostate and zonal segmentations integrated into clinical workflows may facilitate the routine inclusion into clinical reports of quantitative parameters, such as prostate-specific antigen (PSA) density and transition zone PSA density, impacting on the stratification of patients with clinically significant prostate cancer. A further major contribution of this paper to the evolving field of machine learning in prostate MRI is the authors’ initiative to make their segmentations freely available in an online repository, thus supporting research transparency and open science. One limitation of the study that reduces its technical novelty is the use of two-dimensional, instead of threedimensional (3D), segmentation methods. However, 3D deep-learning networks have a very high computational cost, are not easy to integrate into routine clinical workflows using CPU systems, and the paper maintains a clinical applicability, rather than a technical innovation, focus. Articles such as Cuocolo et al should be regarded as steps on the road to a faster adoption of robust machine learning models in prostate MRI while contributing to open science.