Assisted annotation in Deep LOGISMOS: Simultaneous multi‐compartment 3D MRI segmentation of calf muscles

Abstract

BackgroundAutomated 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.PurposeIn 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.MethodsDeep 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.ResultsWe 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 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.ConclusionsOur 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.

Imaging biomarkers in the idiopathic inflammatory myopathies

“…A standardized, validated method for quantifying MRI findings in IIM may help with efficient diagnosis, accurate interpretation of research data, and valid comparisons across studies ( 17 , 63 ). Machine learning, particularly the use of artificial intelligence with deep learning technology, has shown promising results in IIMs and other muscle disorders and has the potential to address some of the limitations of MRI ( 101 – 103 ).…”

Hybrid deep learning and optimal graph search method for optical coherence tomography layer segmentation in diseases affecting the optic nerve

“…2.2 ), a general purpose method for optimally segmenting multiple -D surfaces that mutually interact within individual and/or between objects [ 16 – 19 ]. Since LOGISMOS theoretically guarantees surface/object topology by design, it has been widely used and exceeds segmentation accuracy compared to other standard automated methods [ 20 ] in multi-surface/object segmentation applications. However, the LOGISMOS parameters in OCTExplorer were not tailored for severe retinopathy and/or retinal deformation, resulting in unreliable segmentation results and structural measurements in extreme cases of thickening and thinning of the retinal layers.…”