Hybrid Level-Sets for Vertebral Body Segmentation in Clinical Spine MRI

Hille, Georg; Glaßer, Sylvia; Tönnies, Klaus D.

doi:10.1016/j.procs.2016.07.005

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…By constructing an energy generalization, the contour profile is gradually approached towards the edge of the object to be detected, driven by the minimum value of the energy function, and the target is finally segmented. Georg et al [7] combined active contours and the Chan-Vese intensity model into a level set algorithm to achieve low-quality, diseased MRI image segmentation of the spine. Active contours are often time consuming.…”

Section: Related Work a Spine Segmentation Based On Conventional Mach...mentioning

confidence: 99%

Cobb Angle Measurement Based on Spine Segmentation Using ATT UNet 3+

Peng,

Hu,

Zhang

et al. 2024

IEEE Access

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Scoliosis refers to the abnormal curvature of human spine, which is one of the most common deformities in children and adolescents. The Cobb angle is the gold standard for quantifying the severity of scoliosis and is used to assess the severity of scoliosis. Often the accuracy of the Cobb angle measurement relies on the subjective experience of the doctor and the process is very time consuming. In this study, we propose a new deep neural network, ATT UNet 3+, based on UNet 3+. Our approach incorporates a novel hybrid attention mechanism in the network's upsampling process. This mechanism allows for the appropriate reweighting of fused multi-scale information and facilitates effective supervision of the final output results. The proposed neural network is trained, tested and validated on 155 X-ray ortho-slices. The deep learning network is compared with the more effective neural networks commonly used today. ATT UNet 3+ achieves the best performance in the segmentation evaluation results. Regarding the final Cobb angle calculations, the absolute mean error between the longest distance ellipsoidal point (LDEP) method and expert measurements amounted to 1.6°. ATT UNet 3+ provides a potential tool for segmenting the spine in X-ray, which can improve the efficiency and accuracy of doctors in processing scoliosis pathological images.

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Section: Related Work a Spine Segmentation Based On Conventional Mach...mentioning

confidence: 99%

Cobb Angle Measurement Based on Spine Segmentation Using ATT UNet 3+

Peng,

Hu,

Zhang

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…For instance, Liu et al [21] used level set-based method for segmentation of CT vertebrae images in sagittal, coronal, and axial domain. Hille et al [22] used hybrid level set to perform segmentation of thoracic and lumbar vertebrae in MRI images. Rastgarpour et al [23] combined local region-based level set with fuzzy cluster variation to address the solution of in-homogeneity in medical images for segmentation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For this purpose, level set-based approaches may be beneficial. Previously, these approaches were used for high-level shape extraction for various machine vision and medical imaging tasks [14,[21][22][23]. These techniques are based on active contour models [15].…”

Section: Literature Reviewmentioning

confidence: 99%

A Region-Based Deep Level Set Formulation for Vertebral Bone Segmentation of Osteoporotic Fractures

et al. 2019

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Accurate segmentation of the vertebrae from medical images plays an important role in computer-aided diagnoses (CADs). It provides an initial and early diagnosis of various vertebral abnormalities to doctors and radiologists. Vertebrae segmentation is very important but difficult task in medical imaging due to low-contrast imaging and noise. It becomes more challenging when dealing with fractured (osteoporotic) cases. This work is dedicated to address the challenging problem of vertebra segmentation. In the past, various segmentation techniques of vertebrae have been proposed. Recently, deep learning techniques have been introduced in biomedical image processing for segmentation and characterization of several abnormalities. These techniques are becoming popular for segmentation purposes due to their robustness and accuracy. In this paper, we present a novel combination of traditional region-based level set with deep learning framework in order to predict shape of vertebral bones accurately; thus, it would be able to handle the fractured cases efficiently. We termed this novel Framework as BFU-Net^which is a powerful and practical framework to handle fractured vertebrae segmentation efficiently. The proposed method was successfully evaluated on two different challenging datasets: (1) 20 CT scans, 15 healthy cases, and 5 fractured cases provided at spine segmentation challenge CSI 2014; (2) 25 CT image data (both healthy and fractured cases) provided at spine segmentation challenge CSI 2016 or xVertSeg.v1 challenge. We have achieved promising results on our proposed technique especially on fractured cases. Dice score was found to be 96.4 ± 0.8% without fractured cases and 92.8 ± 1.9% with fractured cases in CSI 2014 dataset (lumber and thoracic). Similarly, dice score was 95.2 ± 1.9% on 15 CT dataset (with given ground truths) and 95.4 ± 2.1% on total 25 CT dataset for CSI 2016 datasets (with 10 annotated CT datasets). The proposed technique outperformed other state-of-the-art techniques and handled the fractured cases for the first time efficiently.

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“…For instance, level set method was adapted by [25] to perform CT vertebral image segmentation in various field of views. [26] Employed hybridized form of level sets to perform vertebrae segmentation. [27] Combined fuzzy cluster variation with region based level set to perform vertebrae segmentation from in-homogenous image dataset.…”

Section: Related Workmentioning

confidence: 99%

A Robust Scheme of Vertebrae Segmentation for Medical Diagnosis

et al. 2019

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Precise and reliable segmentation of vertebral column is crucial for accurate computer aided diagnostic system (CADx). It assists the radiologists and doctors in identifying various pathologies in the vertebrae with better visualizations. Accurate segmentation of vertebral disks and bones from the medical images is a tough job and becomes more challenging when dealing with various deformities and pathologies. While remarkable success was achieved by deep convolutional neural networks (DCNNs) in medical image segmentation, it is still a difficult task for DCNNs to handle the medical image segmentation problems with various deformities and anatomical complexities. In this paper, we propose a novel and efficient framework to address the subject problem by integrating a parametric level set approach in deep convolutional neural networks. The proposed scheme utilizes the probability map of pre-trained deep network to initialize the level set and it refines the output iteratively under the action of various forces to fine-tune the training of deep network. Thus the learning of the network is improved and the network is able to accommodate high topological shape variations in the vertebrae. This proposed method was evaluated on two different datasets. The first one is 20 publically available 3D spine MRI dataset to perform disc segmentation and the second one is 173 computed tomography scans for thoracolumbar (thoracic and lumbar) vertebrae segmentation. The dice score was found to be 90.37 ± 0.9 percent for disks segmentation and 94.7 ± 1.1 with ASSD of 0.1 ± 0.04 mm for thoracolumbar vertebrae segmentation. The results reveal that our proposed method is robust over multiple segmentations and outperformed the recently published state of art methods. INDEX TERMS Intervertebral disk, thoracolumbar vertebrae, computed tomography, vertebrae segmentation, deep learning, computer aided diagnosis.

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Hybrid Level-Sets for Vertebral Body Segmentation in Clinical Spine MRI

Cited by 8 publications

References 18 publications

Cobb Angle Measurement Based on Spine Segmentation Using ATT UNet 3+

Cobb Angle Measurement Based on Spine Segmentation Using ATT UNet 3+

A Region-Based Deep Level Set Formulation for Vertebral Bone Segmentation of Osteoporotic Fractures

A Robust Scheme of Vertebrae Segmentation for Medical Diagnosis

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