AI and Deep Learning for Automated Segmentation and Quantitative Measurement of Spinal Structures in MRI

• URL: http://arxiv.org/abs/2503.11281v3
• Date: Wed, 19 Mar 2025 06:18:20 GMT
• Title: AI and Deep Learning for Automated Segmentation and Quantitative Measurement of Spinal Structures in MRI
• Authors: Praveen Shastry, Bhawana Sonawane, Kavya Mohan, Naveen Kumarasami, Raghotham Sripadraj, Anandakumar D, Keerthana R, Mounigasri M, Kaviya SP, Kishore Prasath Venkatesh, Bargava Subramanian, Kalyan Sivasailam,
• Abstract summary: This study develops an autonomous AI system for segmenting and measuring key spinal structures in MRI scans.<n>The goal is to reduce clinician workload, enhance diagnostic consistency, and improve assessments.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: Accurate spinal structure measurement is crucial for assessing spine health and diagnosing conditions like spondylosis, disc herniation, and stenosis. Manual methods for measuring intervertebral disc height and spinal canal diameter are subjective and time-consuming. Automated solutions are needed to improve accuracy, efficiency, and reproducibility in clinical practice. Purpose: This study develops an autonomous AI system for segmenting and measuring key spinal structures in MRI scans, focusing on intervertebral disc height and spinal canal anteroposterior (AP) diameter in the cervical, lumbar, and thoracic regions. The goal is to reduce clinician workload, enhance diagnostic consistency, and improve assessments. Methods: The AI model leverages deep learning architectures, including UNet, nnU-Net, and CNNs. Trained on a large proprietary MRI dataset, it was validated against expert annotations. Performance was evaluated using Dice coefficients and segmentation accuracy. Results: The AI model achieved Dice coefficients of 0.94 for lumbar, 0.91 for cervical, and 0.90 for dorsal spine segmentation (D1-D12). It precisely measured spinal parameters like disc height and canal diameter, demonstrating robustness and clinical applicability. Conclusion: The AI system effectively automates MRI-based spinal measurements, improving accuracy and reducing clinician workload. Its consistent performance across spinal regions supports clinical decision-making, particularly in high-demand settings, enhancing spinal assessments and patient outcomes.

Related papers

• Dual Attention Driven Lumbar Magnetic Resonance Image Feature Enhancement and Automatic Diagnosis of Herniation [5.762049149293296]
  This paper proposes an innovative automated LDH classification framework. The framework extracts clinically actionable LDH features and generates standardized diagnostic outputs. It achieves an area under the receiver operating characteristic curve (AUC-ROC) of 0.969 and an accuracy of 0.9486 for LDH detection.
  arXiv  Detail & Related papers  (2025-04-28T02:55:59Z)
• Deep Learning-Based Automated Workflow for Accurate Segmentation and Measurement of Abdominal Organs in CT Scans [0.0]
  The purpose of this study is to develop and validate an automated workflow for the segmentation and measurement of abdominal organs in CT scans.<n>The proposed approach offers an automated, efficient, and reliable solution for abdominal organ measurement in CT scans.
  arXiv  Detail & Related papers  (2025-03-13T06:50:44Z)
• Pathology-Guided AI System for Accurate Segmentation and Diagnosis of Cervical Spondylosis [7.81163465956116]
  We developed an AI-assisted Expert-based Diagnosis System that automates both segmentation and diagnosis of cervical spondylosis using MRI.<n>Our system features a pathology-guided segmentation model capable of accurately segmenting key cervical anatomical structures.<n>Our method delivered high accuracy, precision, recall, and F1 scores in herniation localization, K-line status assessment, and T2 hyperintensity detection.
  arXiv  Detail & Related papers  (2025-03-08T07:55:33Z)
• Comprehensive Study on Lumbar Disc Segmentation Techniques Using MRI Data [0.0]
  This study assesses the effectiveness of several sophisticated deep learning architectures, including ResUnext, Ef3 Net, UNet, and TransUNet, for lumbar disk segmentation.<n>Findings indicate that ResUnext achieved the highest segmentation accuracy, with a Pixel Accuracy of 0.9492 and a Dice Coefficient of 0.8425, with TransUNet following closely after.
  arXiv  Detail & Related papers  (2024-12-25T12:54:52Z)
• Reconstruction of 3D lumbar spine models from incomplete segmentations using landmark detection [0.4194295877935868]
  We present a novel method to reconstruct complete 3D lumbar spine models from incomplete 3D vertebral bodies.<n>Our method achieves the registration of the entire lumbar spine, spanning segments L1 to L5, in just 0.14 seconds.
  arXiv  Detail & Related papers  (2024-12-06T14:23:42Z)
• Enhancing Angular Resolution via Directionality Encoding and Geometric Constraints in Brain Diffusion Tensor Imaging [70.66500060987312]
  Diffusion-weighted imaging (DWI) is a type of Magnetic Resonance Imaging (MRI) technique sensitised to the diffusivity of water molecules. This work proposes DirGeo-DTI, a deep learning-based method to estimate reliable DTI metrics even from a set of DWIs acquired with the minimum theoretical number (6) of gradient directions.
  arXiv  Detail & Related papers  (2024-09-11T11:12:26Z)
• A Clinical Guideline Driven Automated Linear Feature Extraction for Vestibular Schwannoma [2.7219200491616378]
  Vestibular Schwannoma is a benign brain tumour that grows from one of the balance nerves. Patients may be treated by surgery, radiosurgery or with a conservative "wait-and-scan" strategy. This work aims to automate and improve this process by using deep learning based segmentation to extract relevant clinical features through computational algorithms.
  arXiv  Detail & Related papers  (2023-10-30T09:54:24Z)
• Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine [72.85011943179894]
  We propose a weakly-supervised deep learning framework that preserves the rigidity and the volume of each vertebra while maximizing the accuracy of the registration. We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI. Our results show that adding the anatomy-aware losses increases the plausibility of the inferred transformation while keeping the accuracy untouched.
  arXiv  Detail & Related papers  (2022-05-16T10:59:55Z)
• SpineNetV2: Automated Detection, Labelling and Radiological Grading Of Clinical MR Scans [70.04389979779195]
  SpineNetV2 is an automated tool which detects and labels vertebral bodies in clinical spinal magnetic resonance (MR) scans. It also performs radiological grading of lumbar intervertebral discs in T2-weighted scans for a range of common degenerative changes.
  arXiv  Detail & Related papers  (2022-05-03T15:05:58Z)
• SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae [54.751251046196494]
  We propose a one-stage detection framework termed SpineOne to simultaneously localize and classify degenerative discs and vertebrae from MRI slices. SpineOne is built upon the following three key techniques: 1) a new design of the keypoint heatmap to facilitate simultaneous keypoint localization and classification; 2) the use of attention modules to better differentiate the representations between discs and vertebrae; and 3) a novel gradient-guided objective association mechanism to associate multiple learning objectives at the later training stage.
  arXiv  Detail & Related papers  (2021-10-28T12:59:06Z)
• iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry [58.943644554192936]
  This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or digital-twins. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients. iPhantom precisely predicted all organ locations with good accuracy of Dice Similarity Coefficients (DSC) >0.6 for anchor organs and DSC of 0.3-0.9 for all other organs.
  arXiv  Detail & Related papers  (2020-08-20T01:50:49Z)
• A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI [70.04389979779195]
  We propose a novel convolutional method for the detection and identification of vertebrae in whole spine MRIs. This involves using a learnt vector field to group detected vertebrae corners together into individual vertebral bodies. We demonstrate the clinical applicability of this method, using it for automated scoliosis detection in both lumbar and whole spine MR scans.
  arXiv  Detail & Related papers  (2020-07-06T09:37:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.