MRI-based Deep Radiomic Phenotyping of Neuromuscular Disorders: A Topology-driven Characterization

• URL: http://arxiv.org/abs/2608.24415v1
• Date: Tue, 25 Aug 2026 11:30:17 GMT
• Title: MRI-based Deep Radiomic Phenotyping of Neuromuscular Disorders: A Topology-driven Characterization
• Abstract summary: This study introduces an automated radiomic phenotyping framework based on original features engineered across five main architectural domains.<n>We map the complex 3D architecture of heterogeneous intramuscular lipodegeneration into objective, morphologically interpretable biomarkers.
• Score: 0.37341173401157257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantitative assessment of muscle MRI is crucial for monitoring neuromuscular disorders (NMD). This study introduces an automated radiomic phenotyping framework based on original features engineered across five main architectural domains: quantitative morphometry, spatial distribution, geometric shape, interactions between progressive fat replacement stages, and graph-based topology. Utilizing 1184 MRI scans from the CoMPaSS-NMD project, we map the complex 3D architecture of heterogeneous intramuscular lipodegeneration into objective, morphologically interpretable biomarkers. We introduce a graph-based skeletonization of fat infiltrates to quantify muscle architectural changes, establishing a multi-dimensional extension of traditional, spatially-agnostic volume metrics by mapping topological networks across the entire 3D muscle volume. Statistical screening via non-parametric Kruskal-Wallis analysis confirmed the discriminative power of these novel descriptors across the genetic hierarchy. Notably, topological network metrics (e.g., SF1_Skel_Nodes, $ε^2$ = 0.2656) and interface dynamics metrics (e.g., SF2_To_SF1_Dist_Min, $ε^2$ = 0.2092) demonstrated substantial effect sizes, providing deeper structural insights than classical volumetric assessments. Post-hoc pairwise evaluations and UMAP projections further indicated the capability of these topological and 3D geometric invariants to capture disease-specific macroscopic infiltration patterns. These results demonstrate that global architectural features represent a highly promising class of biomarkers for differential diagnosis, offering new avenues for tracking longitudinal disease dynamics in neuromuscular diagnostics. The developed automated feature extraction pipeline is integrated and available within the MUSCAT (MUSCle fAt Topology) library.

Related papers

• Predicting Immune Biomarkers with MultiModal Mixture-of-Expert Pathology Foundation Models Empowers Precision Oncology [47.79180201495836]
  MixTIME is a multimodal foundation model that integrates pathology foundation models trained across distinct modalities.<n>It predicts multiplex immunofluorescence (mIF) protein expression from hematoxylin and eosin whole-slide images.<n>MixTIME achieves state-of-the-art performance across 17 protein markers as measured by correlation metrics.
  arXiv  Detail & Related papers  (2026-06-16T16:22:42Z)
• A physics-informed foundation model for quantitative diffusion MRI [46.74037857287099]
  A physics-informed generative microstructure network (PIGMENT) learns a universal generative prior to human brain microstructure and adapts it zero-shot to each participant's measured data to recover subject-specific maps.<n>PIGMENT estimates demonstrated strong biological validity, preserving submillimeter cortical microarchitectural patterns and early-childhood white matter developmental trajectories from 10-fold accelerated scans.<n>These results establish PIGMENT as a physics-informed foundation model that extends quantitative diffusion MRI into regimes traditionally too sparse, heterogeneous, or clinically constrained for reliable analysis.
  arXiv  Detail & Related papers  (2026-05-29T07:26:35Z)
• Modeling Higher-Order Brain Interactions via a Multi-View Information Bottleneck Framework for fMRI-based Psychiatric Diagnosis [37.574253923395105]
  We introduce $O$-information, a signed measure that characterizes the informational nature of HOIs.<n>We integrate third- and fourth-order $O$-information into a unified multi-view information bottleneck framework for fMRI-based psychiatric diagnosis.<n>Our tri-view architecture systematically fuses pairwise, triadic, and tetradic brain interactions, capturing comprehensive brain connectivity.
  arXiv  Detail & Related papers  (2026-04-20T01:50:20Z)
• Hierarchical Mesh Transformers with Topology-Guided Pretraining for Morphometric Analysis of Brain Structures [7.663406601980095]
  We introduce a hierarchical transformer framework for heterogeneous mesh analysis.<n>A feature projection module maps variable-length per-vertex clinical descriptors into the spatial hierarchy.<n>We validate our approach on Alzheimer's disease classification and amyloid burden prediction using volumetric brain meshes from ADNI, as well as focal cortical dysplasia detection on cortical surface meshes from the MELD dataset.
  arXiv  Detail & Related papers  (2026-04-06T22:27:36Z)
• Brain Tumor Classification from 3D MRI Using Persistent Homology and Betti Features: A Topological Data Analysis Approach on BraTS2020 [0.8374077003751697]
  We propose a topology-driven framework for brain tumor classification based on Topological Data Analysis.<n>We analyze 3D Fluid Attenuated Inversion Recovery (FLAIR) images from the BraTS 2020 dataset.<n>We derive a compact set of 100 topological features that summarize the underlying topology of brain tumor structures.
  arXiv  Detail & Related papers  (2026-03-14T05:44:38Z)
• Learning Glioblastoma Tumor Heterogeneity Using Brain Inspired Topological Neural Networks [3.120728330365825]
  TopoGBM is a learning framework designed to capture scanner-robust representations from 3D MRI.<n>Mechanistic interpretability analysis reveals that reconstruction residuals are highly localized to pathologically heterogeneous zones.
  arXiv  Detail & Related papers  (2026-02-11T16:28:13Z)
• Glo-UMF: A Unified Multi-model Framework for Automated Morphometry of Glomerular Ultrastructural Characterization [15.58257979618407]
  Glo-UMF is a unified multi-model framework integrating segmentation, classification, and detection.<n>It is trained on 372 electron microscopy images to quantify glomerular basement membrane thickness, degree of foot process effacement (FPE) and EDD location.
  arXiv  Detail & Related papers  (2025-08-14T05:25:30Z)
• Ensemble Learning and 3D Pix2Pix for Comprehensive Brain Tumor Analysis in Multimodal MRI [2.104687387907779]
  This study presents an integrated approach leveraging the strengths of ensemble learning with hybrid transformer models and convolutional neural networks (CNNs)<n>Our methodology combines robust tumor segmentation capabilities, utilizing axial attention and transformer encoders for enhanced spatial relationship modeling.<n>The results demonstrate outstanding performance, evidenced by quantitative evaluations such as the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD95) for segmentation, and Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Mean-Square Error (MSE) for inpainting.
  arXiv  Detail & Related papers  (2024-12-16T15:10:53Z)
• Patch-based Intuitive Multimodal Prototypes Network (PIMPNet) for Alzheimer's Disease classification [3.144057505325736]
  Part-prototype neural networks integrate the computational advantages of Deep Learning (DL) in an interpretable-by-design architecture. We present PIMPNet, the first interpretable multimodal model for 3D images and demographics applied to the binary classification of Alzheimer's Disease (AD) from 3D sMRI and patient's age.
  arXiv  Detail & Related papers  (2024-07-19T12:58:18Z)
• Anatomy-guided Pathology Segmentation [56.883822515800205]
  We develop a generalist segmentation model that combines anatomical and pathological information, aiming to enhance the segmentation accuracy of pathological features. Our Anatomy-Pathology Exchange (APEx) training utilizes a query-based segmentation transformer which decodes a joint feature space into query-representations for human anatomy. In doing so, we are able to report the best results across the board on FDG-PET-CT and Chest X-Ray pathology segmentation tasks with a margin of up to 3.3% as compared to strong baseline methods.
  arXiv  Detail & Related papers  (2024-07-08T11:44:15Z)
• S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondences [91.48841778012782]
  We propose an unsupervised method to simultaneously learn local and global shape structures across population anatomies. Our pipeline significantly improves unsupervised correspondence estimation for SSMs compared to baseline methods. Our method is robust enough to learn from noisy neural network predictions, potentially enabling scaling SSMs to larger patient populations.
  arXiv  Detail & Related papers  (2023-04-15T09:39:52Z)
• Deep Implicit Statistical Shape Models for 3D Medical Image Delineation [47.78425002879612]
  3D delineation of anatomical structures is a cardinal goal in medical imaging analysis. Prior to deep learning, statistical shape models that imposed anatomical constraints and produced high quality surfaces were a core technology. We present deep implicit statistical shape models (DISSMs), a new approach to delineation that marries the representation power of CNNs with the robustness of SSMs.
  arXiv  Detail & Related papers  (2021-04-07T01:15:06Z)
• Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI [47.26574993639482]
  We show improved anomaly segmentation performance and the general capability to obtain much more crisp reconstructions of input data at native resolution. The modeling of the laplacian pyramid further enables the delineation and aggregation of lesions at multiple scales.
  arXiv  Detail & Related papers  (2020-06-23T09:20:42Z)

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.