Typical prostate MR dataset are 2D with few slices and high in-plane resolution. These dimensions do not conform naturally to traditional 3D convolution neural network up/down-sampling schemes, which customarily double/halves the input dimensions in all directions. We compared two strategies for up/down-sampling applied to prostate segmentation in MRI datasets: 1) Transform the datasets and applying orthodox convolution layers. 2) Adapt the up/down-sampling convolution layers in order to use more native resolution input datasets. Our results show that, in general, the former strategy works best, both in terms training loss convergence and overall segmentation results.
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