Abstract
BACKGROUND: Automated segmentation of individual calf muscle compartments in 3D MR images is gaining importance in diagnosing muscle disease, monitoring its progression, and prediction of the disease course. Although deep convolutional neural networks have ushered in a revolution in medical image segmentation, achieving clinically acceptable results is a challenging task and the availability of sufficiently large annotated datasets still limits their applicability. PURPOSE: In this paper, we present a novel approach combing deep learning and graph optimization in the paradigm of assisted annotation for solving general segmentation problems in 3D, 4D, and generally n-D with limited annotation cost. METHODS: Deep LOGISMOS combines deep-learning-based pre-segmentation of objects of interest provided by our convolutional neural network, FilterNet+, and our 3D multi-objects LOGISMOS framework (layered optimal graph image segmentation of multiple objects and surfaces) that uses newly designed trainable machine-learned cost functions. In the paradigm of assisted annotation, multi-object JEI for efficient editing of automated Deep LOGISMOS segmentation was employed to form a new larger training set with significant decrease of manual tracing effort. RESULTS: We have evaluated our method on 350 lower leg (left/right) T1-weighted MR images from 93 subjects (47 healthy, 46 patients with muscular morbidity) by fourfold cross-validation. Compared with the fully manual annotation approach, the annotation cost with assisted annotation is reduced by 95%, from 8 h to 25 min in this study. The experimental results showed average Dice similarity coefficient (DSC) of 96.56 ± 0.26% and average absolute surface positioning error of 0.63 pixels (0.44 mm) for the five 3D muscle compartments for each leg. These results significantly improve our previously reported method and outperform the state-of-the-art nnUNet method. CONCLUSIONS: Our proposed approach can not only dramatically reduce the expert's annotation efforts but also significantly improve the segmentation performance compared to the state-of-the-art nnUNet method. The notable performance improvements suggest the clinical-use potential of our new fully automated simultaneous segmentation of calf muscle compartments.