Deep Learning Approaches for Lumbar Spine MRI Segmentation and Classification
Bibliographic record
Abstract
Lumbar spine disorders are prevalent conditions that can lead to chronic pain and reduced quality of life, often requiring accurate diagnosis through magnetic resonance imaging.However, manual interpretation of MRI scans is time-intensive and may lead to diagnostic errors due to the complexity of spinal anatomy and subtle differences between healthy and pathological tissues.This study aims to develop an automated method for segmenting and classifying the lumbar spine, including the assessment of lumbar lordosis, using deep learning techniques.In the first stage, spinal segmentation is performed using a combination of image thresholding and a convolutional neural network based on the U-Net architecture, with binary conversion of ground truth images and data augmentation techniques to enhance the dataset.The data is divided into training and testing sets, with 80% allocated for model training.In the second stage, classification of lumbar lordosis is achieved through a method called "Deteclordose" and transfer learning, following the automatic calculation of the Cobb angle.Images are then categorized into three classes: hyper lordosis, hypo lordosis, and normal lordosis.The method was evaluated on a dataset of MRI images obtained from Jordanian hospitals, achieving promising results, including a segmentation accuracy of 99.30%, a loss value of 0.025, a classification accuracy of 96%, a recall of 94%, and an F1 score of 93%.These outcomes demonstrate the potential of the proposed approach to improve diagnostic accuracy and reduce the burden on radiologists by providing a reliable, automated system for analyzing lumbar spine MRI images.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".