Beyond one-hot encoding? Journey into compact encoding for large multi-class segmentation

This work presents novel methods to reduce computational and memory requirements for medical image segmentation with a large number of classes. We curiously observe challenges in maintaining state-of-the-art segmentation performance with all of the explored options. Standard learning-based methods typically employ one-hot encoding of class labels. The computational complexity and memory requirements thus increase linearly with the number of classes. We propose a family of binary encoding approaches instead of one-hot encoding to reduce the computational complexity and memory requirements to logarithmic in the number of classes. In addition to vanilla binary encoding, we investigate the effects of error-correcting output codes (ECOCs), class weighting, hard/soft decoding, class-to-codeword assignment, and label embedding trees. We apply the methods to the use case of whole brain parcellation with 108 classes based on 3D MRI images. While binary encodings have proven efficient in so-called extreme classification problems in computer vision, we faced challenges in reaching state-of-the-art segmentation quality with binary encodings. Compared to one-hot encoding (Dice Similarity Coefficient (DSC) = 82.4 (2.8)), we report reduced segmentation performance with the binary segmentation approaches, achieving DSCs in the range from 39.3 to 73.8. Informative negative results all too often go unpublished. We hope that this work inspires future research of compact encoding strategies for large multi-class segmentation tasks.