Automatic Quadriceps and Patellae Segmentation of MRI with Cascaded U2 -Net and SASSNet Deep Learning Model.

PURPOSE Automatic muscle segmentation is critical for advancing our understanding of human physiology, biomechanics, and musculoskeletal pathologies, as it allows for timely exploration of large multi-dimensional image sets. Segmentation models are rarely developed/validated for the pediatric model. As such, auto-segmentation is not available to explore how muscle architectural changes during development and how disease/pathology affects the developing musculoskeletal system. Thus, we aimed to develop and validate an end-to-end, fully automated, deep learning model for accurate segmentation of the rectus femoris and vastus lateral, medialis, and intermedialis using a pediatric database. METHODS We developed a two-stage cascaded deep learning model in a coarse-to-fine manner. In the first stage, the U2 -Net roughly detects the muscle sub-compartment region. Then, in the second stage, the Shape-aware 3D semantic segmentation method SASSNet refines the cropped target regions to generate the more finer and accurate segmentation masks. We utilized multi-feature image maps in both stages to stabilize performance and validated their use with an ablation study. The second stage SASSNet was independently run and evaluated with three different cropped region resolutions: the original image resolution, and images down-sampled 2x & 4x (high, mid, and low). The relationship between image resolution and segmentation accuracy was explored. In addition, the patella was included as a comparator to past work. We evaluated segmentation accuracy using leave-one-out testing on a database of 3D MR images (0.43×0.43×2mm) from 40 pediatric participants (age 15.3±1.9years, 55.8±11.8kg, 164.2±7.9cm, 38F/2M). RESULTS The mid-resolution second stage produced the best results for the vastus medialis, rectus femoris, and patella (Dice Similarity Coefficient = 95.0%, 95.1%, 93.7%), whereas the low-resolution second stage produced the best results for the vastus lateralis and vastus intermedialis (DSC = 94.5% and 93.7%). In comparing the low- to mid-resolution cases, the vasti intermedialis, vastus medialis, rectus femoris, and patella produced significant differences (p = 0.0015, p = 0.0101, p<0.0001, p = 0.0003) and the vasti lateralis did not (p = 0.2177). The high-resolution Stage2 had significantly lower accuracy (1.0 to 4.4 Dice percentage points) compared to both the mid- and low-resolution routines (p ranged from <0.001 to 0.04). The one exception was the rectus femoris, where there was no difference between the low and high-resolution cases. The ablation study demonstrated that the multi-feature is more reliable than the single feature. CONCLUSIONS  Our successful implementation of this two-stage segmentation pipeline provides a critical tool for expanding pediatric muscle physiology and clinical research. With a relatively small and variable dataset, our fully automatic segmentation technique produces accuracies that matched or exceeded the current state of the art. The two-stage segmentation avoids memory issues and excessive run times by using a first stage focused on cropping out unnecessary data. The excellent Dice similarity coefficients improve upon previous template-based automatic and semi-automatic methodologies targeting the leg musculature. More importantly, with a naturally variable dataset (size, shape, etc.), the proposed model demonstrates slightly improved accuracies, compared to previous neural networks methods. This article is protected by copyright. All rights reserved.