Quantitative techniques for characterizing deep learning (DL) algorithms are necessary to inform their clinical application, use, and quality assurance. This work analyzes the performance of DL algorithms for segmentation in prostate MRI at a population level. We performed computational observer studies and spatial entropy mapping for characterizing the variability of DL segmentation algorithms and evaluated them on a clinical MRI task that informs the treatment and management of prostate cancer patients. Specifically, we analyzed the task of prostate and peri-prostatic anatomy segmentation in prostate MRI and compared human and computer observer populations against one another.
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