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A study of CNN capacity applied to Left Venticle Segmentation in Cardiac MRI

arXiv 2107.01318 ↗First posted 3 Jul 2021eess.IV cs.CV cs.NEcited by 0

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Abstract (arXiv)

CNN (Convolutional Neural Network) models have been successfully used for segmentation of the left ventricle (LV) in cardiac MRI (Magnetic Resonance Imaging), providing clinical measurements. In practice, two questions arise with deployment of CNNs: 1) when is it better to use a shallow model instead of a deeper one? 2) how the size of a dataset might change the network performance? We propose a framework to answer them, by experimenting with deep and shallow versions of three U-Net families, trained from scratch in six subsets varying from 100 to 10,000 images, different network sizes, learning rates and regularization values. 1620 models were evaluated using 5-fold cross-validation by loss and DICE. The results indicate that: sample size affects performance more than architecture or hyper-parameters; in small samples the performance is more sensitive to hyper-parameters than architecture; the performance difference between shallow and deeper networks is not the same across families.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 30 references that are in the map. First posted on arXiv 3 Jul 2021.

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19 of 30 references.

References · 19

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7 more
  1. 2017LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentationcited by 212
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  7. 2020How many images do I need? Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoringcited by 11

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