Deep neural network for automated simultaneous intervertebral disc (IVDs) identification and segmentation of multi-modal MR images

Das, Pabitra; Pal, Chandrajit; Acharyya, Amit; Chakrabarti, Amlan; Basu, Saumyajit

doi:10.1016/j.cmpb.2021.106074

Cited by 17 publications

(12 citation statements)

References 28 publications

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“…The proposed model achieves a recognition accuracy of 94%. The results in the MICCAI IVD 2018 dataset are said to be validated by comparison with other methods [6].…”

Section: Figure 3 Proposed Unet Modelmentioning

confidence: 99%

Lumbar Spinal Stenosis Analysis with Deep Learning Based Decision Support Systems

Altun

Alkan

2023

Gazi University Journal of Science

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Lumbar spinal stenosis is a disease with negative consequences and usually occurs in 3 vertebrae, disc and canal located in the lower back. In this region, the nerves in the canal can be exposed to pressure for various reasons, and diseases occur. Surgical operation is required to treat canal narrowing, and the exact location and size of the spinal stenosis is vital importance for the operation. The UNet model, which is an example of this network, can be further deeper using different deep learning networks. In this study, it is aimed to be the basis for the creation of a system that helps in the diagnosis of canal stenosis by using a deeper network. The ResUNET model, in which ResNet is used as the backbone, achieved an average IoU of 0.987. This result reveals that MR images can be used in segmentation for the diagnosis of Lumbar spinal stenosis.

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“…The proposed model achieves a recognition accuracy of 94%. The results in the MICCAI IVD 2018 dataset are said to be validated by comparison with other methods [6].…”

Section: Figure 3 Proposed Unet Modelmentioning

confidence: 99%

Lumbar Spinal Stenosis Analysis with Deep Learning Based Decision Support Systems

Altun

Alkan

2023

Gazi University Journal of Science

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“…To our knowledge, however, only a handful of studies have simultaneously segmented lumbar structures and their adjacent structures on MR images simultaneously [ 15 , 16 ]. Prior studies and segmentation have focused mainly on bones [ 17 , 18 ], discs [ 19 ], and nerves [ 20 ]. Regrettably, the segmentation of automatic lumbar structures and their adjacent structures in MR images has not been systematically investigated before, specifically for 3D segmentation of the large blood vessels, as well as the psoas major muscle.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Automated Magnetic Resonance Image Segmentation of the Lumbar Structure and Its Adjacent Structures at the L4/5 Level

Wang

Liu

et al. 2023

Bioengineering

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(1) Background: This study aims to develop a deep learning model based on a 3D Deeplab V3+ network to automatically segment multiple structures from magnetic resonance (MR) images at the L4/5 level. (2) Methods: After data preprocessing, the modified 3D Deeplab V3+ network of the deep learning model was used for the automatic segmentation of multiple structures from MR images at the L4/5 level. We performed five-fold cross-validation to evaluate the performance of the deep learning model. Subsequently, the Dice Similarity Coefficient (DSC), precision, and recall were also used to assess the deep learning model’s performance. Pearson’s correlation coefficient analysis and the Wilcoxon signed-rank test were employed to compare the morphometric measurements of 3D reconstruction models generated by manual and automatic segmentation. (3) Results: The deep learning model obtained an overall average DSC of 0.886, an average precision of 0.899, and an average recall of 0.881 on the test sets. Furthermore, all morphometry-related measurements of 3D reconstruction models revealed no significant difference between ground truth and automatic segmentation. Strong linear relationships and correlations were also obtained in the morphometry-related measurements of 3D reconstruction models between ground truth and automated segmentation. (4) Conclusions: We found it feasible to perform automated segmentation of multiple structures from MR images, which would facilitate lumbar surgical evaluation by establishing 3D reconstruction models at the L4/5 level.

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“…Currently, computer vision is widely applied in various fields, including medicine, to diagnose and analyze diseases based on medical image. The disorders diagnosed using medical images include thyroid cancer [ 1 ] on ultrasound images, back pain [ 2 ] on computed tomography scans, breast cancer [ 3 ] on mammograms, dental and oral diseases [ 4 ] on radiographic images, abnormalities of spinal intervertebral discs [ 5 , 6 ] on magnetic resonance image, and retinal diseases—diabetes mellitus [ 7 , 8 ] and glaucoma [ 9 , 10 ]—on fundus images. Glaucoma is an eye disease that may be the second-largest cause of blindness in the world.…”

Section: Introductionmentioning

confidence: 99%

Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images

et al. 2023

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Objectives: The optic disc is part of the retinal fundus image structure, which influences the extraction of glaucoma features. This study proposes a method that automatically segments the optic disc area in retinal fundus images using deep learning based on a convolutional neural network (CNN).Methods: This study used private and public datasets containing retinal fundus images. The private dataset consisted of 350 images, while the public dataset was the Retinal Fundus Glaucoma Challenge (REFUGE). The proposed method was based on a CNN with a single-shot multibox detector (MobileNetV2) to form images of the region-of-interest (ROI) using the original image resized into 640 × 640 input data. A pre-processing sequence was then implemented, including augmentation, resizing, and normalization. Furthermore, a U-Net model was applied for optic disc segmentation with 128 × 128 input data.Results: The proposed method was appropriately applied to the datasets used, as shown by the values of the F1-score, dice score, and intersection over union of 0.9880, 0.9852, and 0.9763 for the private dataset, respectively, and 0.9854, 0.9838 and 0.9712 for the REFUGE dataset.Conclusions: The optic disc area produced by the proposed method was similar to that identified by an ophthalmologist. Therefore, this method can be considered for implementing automatic segmentation of the optic disc area.

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Deep neural network for automated simultaneous intervertebral disc (IVDs) identification and segmentation of multi-modal MR images

Cited by 17 publications

References 28 publications

Lumbar Spinal Stenosis Analysis with Deep Learning Based Decision Support Systems

Lumbar Spinal Stenosis Analysis with Deep Learning Based Decision Support Systems

Deep Learning-Based Automated Magnetic Resonance Image Segmentation of the Lumbar Structure and Its Adjacent Structures at the L4/5 Level

Automatic Method for Optic Disc Segmentation Using Deep Learning on Retinal Fundus Images

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