Investigation and benchmarking of U-Nets on prostate segmentation tasks

• Published in: Computerized medical imaging and graphics 2023-07, Vol.107, p.102241-102241, Article 102241
• Main Authors: Bhandary, Shrajan, Kuhn, Dejan, Babaiee, Zahra, Fechter, Tobias, Benndorf, Matthias, Zamboglou, Constantinos, Grosu, Anca-Ligia, Grosu, Radu
• Format: Article
• Language: English
• Subjects: Algorithms; Automatic prostate segmentation; Benchmarking; Comparison framework; Humans; Image Processing, Computer-Assisted - methods; Male; Medical imaging; Neural Networks, Computer; Prostate - diagnostic imaging; Prostatic Neoplasms - diagnostic imaging; U-net variations
• ISSN: 0895-6111; 1879-0771
• DOI: 10.1016/j.compmedimag.2023.102241

In healthcare, a growing number of physicians and support staff are striving to facilitate personalized radiotherapy regimens for patients with prostate cancer. This is because individual patient biology is unique, and employing a single approach for all is inefficient. A crucial step for customizing radiotherapy planning and gaining fundamental information about the disease, is the identification and delineation of targeted structures. However, accurate biomedical image segmentation is time-consuming, requires considerable experience and is prone to observer variability. In the past decade, the use of deep learning models has significantly increased in the field of medical image segmentation. At present, a vast number of anatomical structures can be demarcated on a clinician’s level with deep learning models. These models would not only unload work, but they can offer unbiased characterization of the disease. The main architectures used in segmentation are the U-Net and its variants, that exhibit outstanding performances. However, reproducing results or directly comparing methods is often limited by closed source of data and the large heterogeneity among medical images. With this in mind, our intention is to provide a reliable source for assessing deep learning models. As an example, we chose the challenging task of delineating the prostate gland in multi-modal images. First, this paper provides a comprehensive review of current state-of-the-art convolutional neural networks for 3D prostate segmentation. Second, utilizing public and in-house CT and MR datasets of varying properties, we created a framework for an objective comparison of automatic prostate segmentation algorithms. The framework was used for rigorous evaluations of the models, highlighting their strengths and weaknesses. [Display omitted] •Model selection for prostate segmentation is difficult due to many U-Net variants.•Investigated state-of-the-art U-Net models to find the best-suited approaches.•Evaluated and benchmarked five U-Net variants on four prostate datasets.•Significant network performance differences observed with small-sized datasets.