Fully automatic segmentation of paraspinal muscles from 3D torso CT images via multi-scale iterative random forest classifications

Kamiya, Naoki; Li, Jing; Kume, Masanori; Fujita, Hiroshi; Shen, Dinggang; Zheng, Guoyan

doi:10.1007/s11548-018-1852-1

Cited by 25 publications

(11 citation statements)

References 27 publications

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“…Xia and colleagues [11] tested several network architectures, including the U-Net as well as novel solutions, to segment psoas major, erector spinae, and multifidus, obtaining excellent Dice similarity scores between 0.913 and 0.95 for the best-performing neural network. Other studies achieved relatively lower performances [21], or focused on different imaging modalities or fields of view [22,23]. In general, it can be concluded that our model achieved state-of-the-art performance and is the only one that has been externally validated on a purposely created dataset so far.…”

Section: Discussionmentioning

confidence: 65%

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An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles

Niemeyer

Zanker

Jonas

et al. 2021

Preprint

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Purpose. Imaging studies about the relevance of muscles in spinal disorders, and sarcopenia in general, require the segmentation of the muscles in the images which is very labour-intensive if performed manually and poses a practical limit to the number of investigated subjects. This study aimed at developing a deep learning-based tool able to fully automatically perform an accurate segmentation of the lumbar muscles in axial MRI scans, and at validating the new tool on an external dataset. Methods. A set of 60 axial MRI images of the lumbar spine was retrospectively collected from a clinical database. Psoas major, quadratus lumborum, erector spinae, and multifidus were manually segmented in all available slices. The dataset was used to train and validate a deep neural network able to segment muscles automatically. Subsequently, the network was externally validated on images purposely acquired from 22 healthy volunteers. Results. The Jaccard index for the individual muscles calculated for the 22 subjects of the external validation set ranged between 0.862 and 0.935, demonstrating a generally excellent performance of the network. Cross-sectional area and fat fraction of the muscles were in agreement with published data. Conclusions. The externally validated deep neural network was able to perform the segmentation of the paravertebral muscles in axial MRI scans in an accurate and fully automated manner, and is therefore a suitable tool to perform large-scale studies in the field of spinal disorders and sarcopenia, overcoming the limitations of non-automated methods.

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Section: Discussionmentioning

confidence: 65%

“…In order to test the performance of the segmentation tool on an external dataset, axial MRI scans of the whole lumbar spine were prospectively collected for 22…”

Section: External Validationmentioning

confidence: 99%

An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles

Niemeyer

Zanker

Jonas

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…In the same context, it is also expected that the diagnosis and classification of joint-specific soft tissue will be improved, owing to the development of deep learning algorithms advantageous for segmentation. Indeed, there are several recent studies that have completed segmentation at a high level [64,65]. In particular, Hashimoto et al and others segmented the psoas major muscle through a U-net-based CNN model, and the trained U-net-based CNN model showed an average of 86.6% intersection over union (IoU).…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future

Lee

Chung

2022

Applied Sciences

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Since its development, deep learning has been quickly incorporated into the field of medicine and has had a profound impact. Since 2017, many studies applying deep learning-based diagnostics in the field of orthopedics have demonstrated outstanding performance. However, most published papers have focused on disease detection or classification, leaving some unsatisfactory reports in areas such as segmentation and prediction. This review introduces research published in the field of orthopedics classified according to disease from the perspective of orthopedic surgeons, and areas of future research are discussed. This paper provides orthopedic surgeons with an overall understanding of artificial intelligence-based image analysis and the information that medical data should be treated with low prejudice, providing developers and researchers with insight into the real-world context in which clinicians are embracing medical artificial intelligence.

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“…Segmentation performances of these CNN structures in different CT images were evaluated using DOCs and the Hausdorff Distance (HD). We defined the automatically segmented set of voxels as AS and the manually defined ground truth as GT [ 16 ]. The DOC quantified the match between two sets by normalising the size of their intersection over the mean of their sizes, defined as follows: where the operator |·| returns the number of voxels contained in a region.…”

Section: Methodsmentioning

confidence: 99%

Section: Model Performance Evaluation and Statistical Analysismentioning

confidence: 99%

Utility of a novel integrated deep convolutional neural network for the segmentation of hip joint from computed tomography images in the preoperative planning of total hip arthroplasty

et al. 2022

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Purpose Preoperative three-dimensional planning is important for total hip arthroplasty. To simulate the placement of joint implants on computed tomography (CT), pelvis and femur must be segmented. Accurate and rapid segmentation of the hip joint is challenging. This study aimed to develop a novel deep learning network, named Changmugu Net (CMG Net), which could achieve accurate segmentation of the femur and pelvis. Methods The overall deep neural network architecture of CMG Net employed three interrelated modules. CMG Net included the 2D U-net to separate the bony and soft tissues. The modular hierarchy method was used for the main femur segmentation to achieve better performance. A layer classifier was adopted to localise femur layers among a series of CT scan images. The first module was a modified 2D U-net, which separated bony and soft tissues; it provided intermediate supervision for the main femur segmentation. The second module was the main femur segmentation, which was used to distinguish the femur from the acetabulum. The third module was the layer classifier, which served as a post-processor for the second module. Results There was a much greater overlap in accuracy results with the “gold standard” segmentation than with competing networks. The dice overlap coefficient was 93.55% ± 5.57%; the mean surface distance was 1.34 ± 0.24 mm, and the Hausdorff distance was 4.19 ± 1.04 mm in the normal and diseased hips, which indicated greater accuracy than the other four competing networks. Moreover, the mean segmentation time of CMG Net was 25.87 ± 2.73 s, which was shorter than the times of the other four networks. Conclusions The prominent segmentation accuracy and run-time of CMG Net suggest that it is a reliable method for clinicians to observe anatomical structures of the hip joints, even in severely diseased cases.

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Fully automatic segmentation of paraspinal muscles from 3D torso CT images via multi-scale iterative random forest classifications

Abstract: Our fully automatic, learning-based method can accurately segment paraspinal muscles from 3D torso CT images. It generates segmentation results that are better than those achieved by the state-of-the-art methods.

Cited by 25 publications

References 27 publications

An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles

An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles

Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future

Utility of a novel integrated deep convolutional neural network for the segmentation of hip joint from computed tomography images in the preoperative planning of total hip arthroplasty

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