arXiv AI

Dense Representation Learning and Differentiable Shape Modeling for Spine Landmark Detection

The paper introduces a method for detecting spine landmarks in X-ray images by treating landmark detection as a pixel-level contrastive representation learning task. Using a generic U‑Net backbone, the approach generates more discriminative heatmaps than traditional heatmap regression, enabling better differentiation between vertebral corners. The method further incorporates learnable shape priors to resolve location ambiguities, achieving competitive accuracy on the AASCE dataset and two private datasets.

arXiv Computer Vision
Oct 22

SpineContextResUNet: A Computationally Efficient Residual UNet for Spine CT Segmentation

SpineContextResUNet is a lightweight 3D Residual U‑Net that uses a Context Block with parallel multi‑dilated convolutions to efficiently capture long‑range anatomical dependencies for spine CT segmentation. It achieves Dice scores of 88.17% on VerSe2020 and 88.13% on CTSpine1K while maintaining a small ~1.7 M parameter footprint. Compared to resource‑heavy Transformers and baselines that fail on commodity hardware, SpineContextResUNet delivers robust inference on edge devices such as the Nvidia Jetson Orin Nano.

By K S Nithurshen, Saurabh J. Shigwan
arXiv AI
Jul 7

Tracing 3D Anatomy in 2D Strokes: A Multi-Stage Projection Driven Approach to Cervical Spine Fracture Identification

arXiv:2601. 15235v4 Announce Type: replace-cross Abstract: Cervical spine fractures require rapid and accurate diagnosis, yet automatic CT interpretation remains challenging as subtle injuries must be assessed across large 3D volumes.

By Fabi Nahian Madhurja, Rusab Sarmun, Muhammad E. H. Chowdhury, Adam Mushtak, Israa Al-Hashimi, Sohaib Bassam Zoghoul
arXiv Machine Learning
Aug 4

SCALP: Semi-Supervised Statistical Shape Modeling from Imperfect 3D Photogrammetry via Landmark-Anchored Spectral Warp

arXiv:2608. 00187v1 Announce Type: cross Abstract: Correspondence-based statistical shape modeling (SSM) is vital for population-level morphometric analysis, but conventional pipelines assume clean, fully registered surfaces.

By Nawazish Khan, Sanjay Bhandari, Sarang Joshi, Alzbeta Novotna, Tiffany Jeong, Loretta Bowman, Michael Hernandez, Tobi Somorin, Viraj Govani, Jesse Glodstein, Shireen Elhabian