Computer-Aided Diagnosis for Determining Sagittal Spinal Curvatures Using Deep Learning and Radiography

Lee, Hyo Min; Kim, Young Jae; Cho, Je Bok; Jeon, Ji Young; Kim, Kwang Gi

doi:10.1007/s10278-022-00592-0

Cited by 7 publications

(2 citation statements)

References 36 publications

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“…As some early studies have shown, CAD algorithms for spinal tissues require segmentation and detection. 36,37 There are several typical examples, such as the analysis of spinal curvatures after segmentation using U-net network, 37 and detection of spinal nerves using the YOHO network under the endoscopic spinal image. 36 These studies of CNN-based CAD algorithms can achieve appreciable accuracy rates and are promising for practical applications.…”

Section: Discussionmentioning

confidence: 99%

A Lightweight Convolutional Neural Network Based on Dynamic Level‐Set Loss Function for Spine MR Image Segmentation

et al. 2023

Magnetic Resonance Imaging

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BackgroundSpine MR image segmentation is important foundation for computer‐aided diagnostic (CAD) algorithms of spine disorders. Convolutional neural networks segment effectively, but require high computational costs.PurposeTo design a lightweight model based on dynamic level‐set loss function for high segmentation performance.Study TypeRetrospective.PopulationFour hundred forty‐eight subjects (3163 images) from two separate datasets. Dataset‐1: 276 subjects/994 images (53.26% female, mean age 49.02 ± 14.09), all for disc degeneration screening, 188 had disc degeneration, 67 had herniated disc. Dataset‐2: public dataset with 172 subjects/2169 images, 142 patients with vertebral degeneration, 163 patients with disc degeneration.Field Strength/SequenceT2 weighted turbo spin echo sequences at 3T.AssessmentDynamic Level‐set Net (DLS‐Net) was compared with four mainstream (including U‐net++) and four lightweight models, and manual label made by five radiologists (vertebrae, discs, spinal fluid) used as segmentation evaluation standard. Five‐fold cross‐validation are used for all experiments. Based on segmentation, a CAD algorithm of lumbar disc was designed for assessing DLS‐Net's practicality, and the text annotation (normal, bulging, or herniated) from medical history data were used as evaluation standard.Statistical TestsAll segmentation models were evaluated with DSC, accuracy, precision, and AUC. The pixel numbers of segmented results were compared with manual label using paired t‐tests, with P < 0.05 indicating significance. The CAD algorithm was evaluated with accuracy of lumbar disc diagnosis.ResultsWith only 1.48% parameters of U‐net++, DLS‐Net achieved similar accuracy in both datasets (Dataset‐1: DSC 0.88 vs. 0.89, AUC 0.94 vs. 0.94; Dataset‐2: DSC 0.86 vs. 0.86, AUC 0.93 vs. 0.93). The segmentation results of DLS‐Net showed no significant differences with manual labels in pixel numbers for discs (Dataset‐1: 1603.30 vs. 1588.77, P = 0.22; Dataset‐2: 863.61 vs. 886.4, P = 0.14) and vertebrae (Dataset‐1: 3984.28 vs. 3961.94, P = 0.38; Dataset‐2: 4806.91 vs. 4732.85, P = 0.21). Based on DLS‐Net's segmentation results, the CAD algorithm achieved higher accuracy than using non‐cropped MR images (87.47% vs. 61.82%).Data ConclusionThe proposed DLS‐Net has fewer parameters but achieves similar accuracy to U‐net++, helps CAD algorithm achieve higher accuracy, which facilitates wider application.Evidence Level2Technical EfficacyStage 1

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Section: Discussionmentioning

confidence: 99%

A Lightweight Convolutional Neural Network Based on Dynamic Level‐Set Loss Function for Spine MR Image Segmentation

et al. 2023

Magnetic Resonance Imaging

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“…The Cobb method is used to quantify and categorise lateral and sagittal curvatures of the spine in X-rays. In particular, computer-aided approaches to determine the Cobb angle achieve consistent and reliable results [12][13][14]. However, the high reliability during a radiological examination goes hand in hand with an increased radiation dose for the patient, which can consequently lead to long-term damage, such as cancer [15,16].…”

Section: Introductionmentioning

confidence: 99%

Torsobarography: Intra-Observer Reliability Study of a Novel Posture Analysis Based on Pressure Distribution

Stecher,

Heinke,

Żurawski

et al. 2024

Sensors

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Postural deformities often manifest themselves in a sagittal imbalance and an asymmetric morphology of the torso. As a novel topographic method, torsobarography assesses the morphology of the back by analysing pressure distribution along the torso in a lying position. At torsobarography’s core is a capacitive pressure sensor array. To evaluate its feasibility as a diagnostic tool, the reproducibility of the system and extracted anatomical associated parameters were evaluated on 40 subjects. Landmarks and reference distances were identified within the pressure images. The examined parameters describe the shape of the spine, various structures of the trunk symmetry, such as the scapulae, and the pelvic posture. The results showed that the localisation of the different structures performs with a good (ICC > 0.75) to excellent (ICC > 0.90) reliability. In particular, parameters for approximating the sagittal spine shape were reliably reproduced (ICC > 0.83). Lower reliability was observed for asymmetry parameters, which can be related to the low variability within the subject group. Nonetheless, the reliability levels of selected parameters are comparable to commercial systems. This study demonstrates the substantial potential of torsobarography at its current stage for reliable posture analysis and may pave the way as an early detection system for postural deformities.

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Video-Based Gait Analysis for Spinal Deformity

Suman

Verlekar

2023

Lecture Notes in Computer Science

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Computer-Aided Diagnosis for Determining Sagittal Spinal Curvatures Using Deep Learning and Radiography

Cited by 7 publications

References 36 publications

A Lightweight Convolutional Neural Network Based on Dynamic Level‐Set Loss Function for Spine MR Image Segmentation

A Lightweight Convolutional Neural Network Based on Dynamic Level‐Set Loss Function for Spine MR Image Segmentation

Torsobarography: Intra-Observer Reliability Study of a Novel Posture Analysis Based on Pressure Distribution

Video-Based Gait Analysis for Spinal Deformity

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