Single annotator versus multi-annotator: Challenge of segmenting two neighboring hippocampus head and body with high precision

Abstract

Background and objectiveThis work investigates multi-annotator segmentation framework for semantic image segmentation using deep learning approaches. MethodsThe framework consists of various deep learning models which perform independently, and then their decisions are fused by a combiner network. The underlying idea of the multi-annotator (ensemble) segmentation framework is to take advantage of the synergistic effect created by the complementary information provided by the different annotators to reach a peak performance superior to each of the annotators when performing alone. The performance of the multi-annotator framework was assessed for the challenging task of the hippocampus head and body segmentation from MR images. Atlas-based segmentation method, deep learning-based multi-view segmentation approach, as well as different decision fusion algorithms, such as STAPLE and shape-based averaging, were assessed versus the proposed multi-annotator segmentation framework. ResultsAmong the different deep learning architectures and loss functions, ResNet model with cross-entropy loss function (ResNet-CE) exhibited the best performance (best performing single model) with a Dice of 88.6 ± 1.8 % and 88.6 ± 1.8 % for the hippocampus head and body, respectively. However, the multi-annotator segmentation framework (which also involved Resnet-CE as one of the annotators) exhibited superior performance with Dice of 91.1 ± 1.3 % (head) and 91.0 ± 1.3 % (body) compared to the atlas-based method with Dice of 88.5 ± 1.5 % (head) and 88.4 ± 1.5 % (body) and multi-view approach with Dice of 89.0 ± 1.4 % (head) and 88.9 ± 1.5 % (body). ConclusionThe multi-annotator segmentation framework exhibits superior performance over each annotator alone which could be employed almost for all machine learning-related problems to enhance the overall effectiveness of the deep/machine learning algorithms.