TY  - JOUR
AU  - Bhandary, Shrajan
AU  - Kuhn, Dejan
AU  - Babaiee, Zahra
AU  - Fechter, Tobias
AU  - Benndorf, Matthias
AU  - Zamboglou, Constantinos
AU  - Grosu, Anca-Ligia
AU  - Grosu, Radu
TI  - Investigation and benchmarking of U-Nets on prostate segmentation tasks.
JO  - Computerized medical imaging and graphics
VL  - 107
SN  - 0895-6111
CY  - Amsterdam [u.a.]
PB  - Elsevier Science
M1  - DKFZ-2023-01008
SP  - 102241
PY  - 2023
AB  - 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.
KW  - Automatic prostate segmentation (Other)
KW  - Comparison framework (Other)
KW  - Medical imaging (Other)
KW  - U-net variations (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:37201475
DO  - DOI:10.1016/j.compmedimag.2023.102241
UR  - https://inrepo02.dkfz.de/record/276108
ER  -