The total kidney volume (TKV) increases with ADPKD progression and hence, can be used to quantify disease progression. The TKV calculation requires accurate delineation of kidney volumes which is usually performed manually by an expert physician. However, this is time consuming and automated segmentation is warranted, e.g., using deep learning. The implementation of the latter is usually hindered due to a lack of large, annotated datasets.In this work, we address this problem by implementing the cosine loss function and a technique called Sharpness Aware Minimization (SAM) into the U-Net to improve TKV estimation in small sized datasets.
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