Related papers: Diffusion-Based Semantic Segmentation of Lumbar Spine MRI Scans of Lower Back Pain Patients

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Enhancing Low Back Pain Assessment with Diffusion Models for Lumbar Spine MRI Segmentation [13.36450508810551]
This study introduces a diffusion-based framework for robust and accurate semantic segmentation of lumbar spine MRI scans from patients with low back pain (LBP)<n>We compared with advanced models for segmenting vertebrae, intervertebral discs (IVDs), and spinal canal using the SPIDER dataset.<n>The results showed that SpineSegDiff achieved a segmentation performance comparable to that of the state-of-the-art non-diffusion nnUnet, particularly in improving the identification of degenerated IVDs.
arXiv Detail & Related papers (2026-08-05T14:35:27Z)
PhenSPINE: A Standardized Benchmark for Spine Pathology Diagnosis [4.172199727841524]
This study presents a Magnetic Resonance Imaging dataset of 16,813 images from 250 patients, curated to facilitate advanced deep learning research.<n>We propose a robust diagnostic benchmark that integrates state-of-theart convolutional backbones with a Positional PINE mechanism to explicitly model the anatomical context of intervertebral discs.<n>We find that multisequence fusion strategies yield inferior performance compared to this single-sequence baseline, as the images across sequences in our dataset are significantly compromised by noise interference from surrounding anatomical regions.
arXiv Detail & Related papers (2026-07-22T02:55:36Z)
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We adapt 3D Gaussian Splatting to form a reconstruction starting from sparse anisotropic MRIs, and imaging planes aligned with the anatomy relevant for clinical evaluation are then sampled and rendered.<n>The novel view plane is optimal for diagnostic radiological grading of the target anatomy, whereas the original MRI is not.
arXiv Detail & Related papers (2026-06-24T18:00:03Z)
SpineReport: Automated 3D Quantification and Reporting of Lumbar Spine Degeneration on MRI [0.38696580294804606]
We introduce SpineReport, an open-source, fully automated framework for comprehensive 3D morphometric analysis of lumbar MRI spine.<n>The method extracts quantitative metrics from key structures, including the spinal canal, spinal cord, vertebrae, intervertebral discs, and foramina.<n> Metrics showed strong associations with central canal severity stenosis, with T2-weighted CSF signal providing the highest performance.
arXiv Detail & Related papers (2026-06-08T18:07:45Z)
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Low-dose protocols often result in highly sparse CT volumes with poor through-plane resolution.<n>We propose ReBrain, a retrieval-augmented diffusion framework for brain MRI reconstruction.<n>Experiments on SynthRAD2023 and BraTS demonstrate that ReBrain achieves state-of-the-art performance in cross-modal reconstruction under sparse conditions.
arXiv Detail & Related papers (2025-11-21T09:18:35Z)
XAttn-BMD: Multimodal Deep Learning with Cross-Attention for Femoral Neck Bone Mineral Density Estimation [4.192785353394277]
Poor bone health is a significant public health concern, and low bone mineral density leads to an increased fracture risk.<n>We present XAttn-BMD, a multimodal deep learning framework that predicts femoral neck BMD from hip X-ray images and structured clinical metadata.<n>It utilizes a novel bidirectional cross-attention mechanism to dynamically integrate image and metadata features for cross-modal mutual reinforcement.
arXiv Detail & Related papers (2025-11-18T15:53:42Z)
Modality-agnostic, patient-specific digital twins modeling temporally varying digestive motion [8.884066499888718]
Clinical implementation of deformable image registration (DIR) requires voxel-based spatial accuracy metrics.<n>Patient-specific digital twins (DTs) modeling temporally varying motion were created to assess the accuracy of DIR methods.
arXiv Detail & Related papers (2025-07-02T17:22:47Z)
M-SCAN: A Multistage Framework for Lumbar Spinal Canal Stenosis Grading Using Multi-View Cross Attention [0.0]
This paper introduces a novel and efficient deep-learning framework that fully automates the grading of lumbar spinal canal stenosis. We demonstrate state-of-the-art performance in grading spinal canal stenosis on a dataset of 1,975 unique studies.
arXiv Detail & Related papers (2025-03-03T15:10:40Z)
Distilling Missing Modality Knowledge from Ultrasound for Endometriosis Diagnosis with Magnetic Resonance Images [18.191192167065555]
The pouch of Douglas (POD) obliteration can be diagnosed using Transvaginal gynecological ultrasound (TVUS) scans and magnetic resonance imaging (MRI) TVUS and MRI are complementary non-invasive endometriosis diagnosis imaging techniques, but patients are usually not scanned using both modalities. We propose in this paper a knowledge distillation training algorithm to improve the POD obliteration detection from MRI.
arXiv Detail & Related papers (2023-07-05T03:08:38Z)
Robust Fiber ODF Estimation Using Deep Constrained Spherical Deconvolution for Diffusion MRI [7.9283612449524155]
A common practice to model the measured DW-MRI signal is via fiber orientation distribution function (fODF) measurement variabilities (e.g., inter- and intra-site variability, hardware performance, and sequence design) are inevitable during the acquisition of DW-MRI. Most existing model-based methods (e.g., constrained spherical deconvolution (CSD)) and learning based methods (e.g., deep learning (DL)) do not explicitly consider such variabilities in fODF modeling. We propose a novel data-driven deep constrained spherical deconvolution method to
arXiv Detail & Related papers (2023-06-05T14:06:40Z)
DDM$^2$: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models [0.3149883354098941]
We propose a self-supervised denoising method for MRI denoising using diffusion denoising generative models. Our framework integrates statistic-based denoising theory into diffusion models and performs denoising through conditional generation.
arXiv Detail & Related papers (2023-02-06T18:56:39Z)
Context-Aware Transformers For Spinal Cancer Detection and Radiological Grading [70.04389979779195]
This paper proposes a novel transformer-based model architecture for medical imaging problems involving analysis of vertebrae. It considers two applications of such models in MR images: (a) detection of spinal metastases and the related conditions of vertebral fractures and metastatic cord compression. We show that by considering the context of vertebral bodies in the image, SCT improves the accuracy for several gradings compared to previously published model.
arXiv Detail & Related papers (2022-06-27T10:31:03Z)
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)
Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers [72.047680167969]
This article aims to introduce the deep learning based data driven techniques for fast MRI including convolutional neural network and generative adversarial network based methods. We will detail the research in coupling physics and data driven models for MRI acceleration. Finally, we will demonstrate through a few clinical applications, explain the importance of data harmonisation and explainable models for such fast MRI techniques in multicentre and multi-scanner studies.
arXiv Detail & Related papers (2022-04-01T22:48:08Z)
Outlier-based Autism Detection using Longitudinal Structural MRI [6.311381904410801]
This paper proposes structural Magnetic Resonance Imaging (sMRI)-based Autism Spectrum Disorder diagnosis via an outlier detection approach. Generative Adversarial Network (GAN) is trained exclusively with sMRI scans of healthy subjects. Experiments reveal that our ASD detection framework performs comparably with the state-of-the-art with far fewer training data.
arXiv Detail & Related papers (2022-02-21T04:37:25Z)
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)
Stacked Hourglass Network with a Multi-level Attention Mechanism: Where to Look for Intervertebral Disc Labeling [2.3848738964230023]
We propose a stacked hourglass network with multi-level attention mechanism to jointly learn intervertebral disc position and their skeleton structure. The proposed deep learning model takes into account the strength of semantic segmentation and pose estimation technique to handle the missing area and false positive detection.
arXiv Detail & Related papers (2021-08-14T14:53:27Z)
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)
Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE) [50.65891535040752]
We propose a diffusion encoding scheme, called Slice-Interleaved Diffusion. SIDE, that interleaves each diffusion-weighted (DW) image volume with slices encoded with different diffusion gradients. We also present a method based on deep learning for effective reconstruction of DW images from the highly slice-undersampled data.
arXiv Detail & Related papers (2020-02-25T14:48:17Z)

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