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Cheng R, Crouzier M, Hug F, Tucker K, Juneau P, McCreedy E, Gandler W, McAuliffe MJ, Sheehan FT. Automatic quadriceps and patellae segmentation of MRI with cascaded U 2 -Net and SASSNet deep learning model. Med Phys 2022; 49:443-460. [PMID: 34755359 PMCID: PMC8758556 DOI: 10.1002/mp.15335] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 10/18/2021] [Accepted: 10/20/2021] [Indexed: 01/03/2023] Open
Abstract
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, autosegmentation 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 subcompartment 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 multifeature 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 downsampled 2× and 4× (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 × 2 mm) from 40 pediatric participants (age 15.3 ± 1.9 years, 55.8 ± 11.8 kg, 164.2 ± 7.9 cm, 38F/2 M). 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 stage 2 had significantly lower accuracy (1.0 to 4.4 dice percentage points) compared to both the mid- and low-resolution routines (p value 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 multifeature 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 semiautomatic 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.
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Affiliation(s)
- Ruida Cheng
- Scientific Application Services (SAS), Office of Scientific Computing Services (OSCS), Office of Intramural Research, Center of Information Technology, NIH, Bethesda, MD, USA
| | - Marion Crouzier
- University of Nantes, Movement, Interactions, Performance, MIP, EA 4334, F-44000 Nantes, France,The University of Queensland, School of Biomedical Sciences, Brisbane
| | - François Hug
- Institut Universitaire de France (IUF), Paris, France,Université Côte d’Azur, LAMHESS, Nice, France
| | - Kylie Tucker
- The University of Queensland, School of Biomedical Sciences, Brisbane
| | - Paul Juneau
- NIH Library, Office of Research Services, National Institutes of Health, Bethesda, MD, USA
| | - Evan McCreedy
- Scientific Application Services (SAS), Office of Scientific Computing Services (OSCS), Office of Intramural Research, Center of Information Technology, NIH, Bethesda, MD, USA
| | - William Gandler
- Scientific Application Services (SAS), Office of Scientific Computing Services (OSCS), Office of Intramural Research, Center of Information Technology, NIH, Bethesda, MD, USA
| | - Matthew J. McAuliffe
- Scientific Application Services (SAS), Office of Scientific Computing Services (OSCS), Office of Intramural Research, Center of Information Technology, NIH, Bethesda, MD, USA
| | - Frances T. Sheehan
- Rehabilitation Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD, USA
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