SpineParseNet: Spine Parsing for Volumetric MR Image by a Two-Stage Segmentation Framework With Semantic Image Representation

Pang, Shumao; Pang, Chunlan; Zhao, Lei; Chen, Yangfan; Su, Zhihai; Zhou, Yujia; Huang, Meiyan; Yang, Wei; Lü, Hai; Feng, Qianjin

doi:10.1109/tmi.2020.3025087

Cited by 110 publications

(59 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the segmentation results (89.8% DSC) are not impressive, indicating that the ISA approach is likely to be misclassified. SpineParseNet [31] achieved a mean DSC of 87.3%, but several limitations exist in this work. First, it performed inferior segmentation due to the blurry boundary.…”

Section: Discussionmentioning

confidence: 94%

“…Vertebrae localization and identification method in CT images are proposed in [37] by combining short and longrange contextual information in a supervised manner to develop a multi-task 3D FCN. A two-stage multi-class segmentation architecture based on a 3D graph convolutional segmentation network (GCSN) for 3D coarse segmentation and a 2D residual U-Net (ResUNet) for 2D segmentation refinement model is presented in [31], but several limitations exist in these works. Inferior segmentation is produced due to the blurry boundary, and it came at a high computational cost due to its complex network model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

OP-convNet: A Patch Classification-Based Framework for CT Vertebrae Segmentation

et al. 2021

View full text Add to dashboard Cite

Accurate vertebrae segmentation from medical images plays an important role in clinical tasks of surgical planning, diagnosis, kyphosis, scoliosis, degenerative disc disease, spondylolisthesis, and postoperative assessment. Although the structures of bone have high contrast in medical images, vertebrae segmentation is a challenging task due to its complex structure, abnormal spine curves, and unclear boundaries. In recent years, deep learning has been widely applied in the segmentation of vertebrae images. In this paper, towards a robust and automatic segmentation system, we present an overlapping patch-based convNet (OP-convNet) model for automatic vertebrae CT images segmentation. Due to the greater memory and processing costs associated with 3D convolutional neural networks, as well as the risk of over-fitting, we employ overlapping patches in segmentation tasks using 2D convNet. In the proposed vertebrae segmentation method, OP-convNet effectively keeps the local information contained in CT images. We divide CT image slices into equal-sized square overlapping patches and applied the RUS-function on these patches for class balancing to minimize computational requirements. Then, these patches are input into the model along with their corresponding ground truth patches. This method has been evaluated on publicly available CT images from the MICCAI CSI workshop challenge. The results indicate that OP-convNet has precision (PRE) of 90.1%, specificity (SPE) of 99.4%, accuracy (ACC) of 98.8%, F-score of 90.1% in terms of the patch-based classification accuracy, and BF-score of 90.2%, sensitivity (SEN) of 90.3%, Jaccard index (JAC) of 82.3%, dice similarity score (DSC) of 89.9% in terms of the segmentation accuracy that outperform previous methods across all metrics.

show abstract

Section: Discussionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

OP-convNet: A Patch Classification-Based Framework for CT Vertebrae Segmentation

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In this study, the Dice similarity coefficient (DSC) [60], Jaccard similarity coefficient (JSC) [61], precision (PRE), and sensitivity (SEN) were used as quantitative assessment metrics to evaluate segmentation performance [20,29]. We evaluated true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) by comparing the true labels with predicted labels:…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…As a result, they may be unable to handle more complicated cases where spine pathologies and curvatures are present. In recent years, deep learning has become a research hotspot in medical image analysis [18] because of its high feature extraction ability [19][20][21][22][23][24]. Deep neural networks (DNNs) often use successful tools as an extractor of high-level features.…”

Section: Introductionmentioning

confidence: 99%

SVseg: Stacked Sparse Autoencoder-Based Patch Classification Modeling for Vertebrae Segmentation

et al. 2022

View full text Add to dashboard Cite

Precise vertebrae segmentation is essential for the image-related analysis of spine pathologies such as vertebral compression fractures and other abnormalities, as well as for clinical diagnostic treatment and surgical planning. An automatic and objective system for vertebra segmentation is required, but its development is likely to run into difficulties such as low segmentation accuracy and the requirement of prior knowledge or human intervention. Recently, vertebral segmentation methods have focused on deep learning-based techniques. To mitigate the challenges involved, we propose deep learning primitives and stacked Sparse autoencoder-based patch classification modeling for Vertebrae segmentation (SVseg) from Computed Tomography (CT) images. After data preprocessing, we extract overlapping patches from CT images as input to train the model. The stacked sparse autoencoder learns high-level features from unlabeled image patches in an unsupervised way. Furthermore, we employ supervised learning to refine the feature representation to improve the discriminability of learned features. These high-level features are fed into a logistic regression classifier to fine-tune the model. A sigmoid classifier is added to the network to discriminate the vertebrae patches from non-vertebrae patches by selecting the class with the highest probabilities. We validated our proposed SVseg model on the publicly available MICCAI Computational Spine Imaging (CSI) dataset. After configuration optimization, our proposed SVseg model achieved impressive performance, with 87.39% in Dice Similarity Coefficient (DSC), 77.60% in Jaccard Similarity Coefficient (JSC), 91.53% in precision (PRE), and 90.88% in sensitivity (SEN). The experimental results demonstrated the method’s efficiency and significant potential for diagnosing and treating clinical spinal diseases.

show abstract

“…Although the methods mentioned above are relatively simple to implement, using these manual segmentation methods to segment small organs in the CT volumes is time-consuming and not task-specific [ 11 ], which requires a sophisticated knowledge base of anatomy. In addition, small organ segmentation tasks require high precision, and in that case, these traditional methods are not suitable.…”

Section: Introductionmentioning

confidence: 99%

Automatic Semicircular Canal Segmentation of CT Volumes Using Improved 3D U‐Net with Attention Mechanism

Liu

Chen

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The vestibular system is the sensory apparatus that helps the body maintain its postural equilibrium, and semicircular canal is an important organ of the vestibular system. The semicircular canals are three membranous tubes, each forming approximately two-thirds of a circle with a diameter of approximately 6.5 mm, and segmenting them accurately is of great benefit for auxiliary diagnosis, surgery, and treatment of vestibular disease. However, the semicircular canal has small volume, which accounts for less than 1% of the overall computed tomography image. Doctors have to annotate the image in a slice-by-slice manner, which is time-consuming and labor-intensive. To solve this problem, we propose a novel 3D convolutional neural network based on 3D U-Net to automatically segment the semicircular canal. We added the spatial attention mechanism of 3D spatial squeeze and excitation modules, as well as channel attention mechanism of 3D global attention upsample modules to improve the network performance. Our network achieved an average dice coefficient of 92.5% on the test dataset, which shows competitive performance in semicircular canals segmentation task.

show abstract

SpineParseNet: Spine Parsing for Volumetric MR Image by a Two-Stage Segmentation Framework With Semantic Image Representation

Cited by 110 publications

References 26 publications

OP-convNet: A Patch Classification-Based Framework for CT Vertebrae Segmentation

OP-convNet: A Patch Classification-Based Framework for CT Vertebrae Segmentation

SVseg: Stacked Sparse Autoencoder-Based Patch Classification Modeling for Vertebrae Segmentation

Automatic Semicircular Canal Segmentation of CT Volumes Using Improved 3D U‐Net with Attention Mechanism

Contact Info

Product

Resources

About