arXiv Computer Vision

Repeatability Characterisation and Error Budget of a Consumer Structured-Light Scanner for 3-D Wound Geometry:A Rigid-Phantom Study

arXiv Computer Vision
Sep 15

Automated Perceptually-Motivated Assessment of Photographic Consistency in Paired Clinical Photographs: Pipeline Development and Internal Evaluation

arXiv:2609.15144v1 Announce Type: new Abstract: Purpose: Paired pre- and post-operative photographs are the standard unit of evidence for plastic surgical outcomes, yet no objective metric verifies w...

By Derrick Lin, Samantha Rabinovich, Joclin Rabinovich, Kassra Garoosi, Sumun Khetpal, Evan Delanoy, Neel Bhardwaj, Jason Roostaeian
arXiv AI
Sep 10

ARNAI: Artifact Removal Network based on Autoencoding and Inpainting for Robust Spinal Image Segmentation and Measurement

The study introduces the RSM framework, which includes the ARNAI artifact removal network, to enhance automated measurement of spinopelvic parameters on postoperative lumbar spine radiographs containing implants. Adding ARNAI to the Transformer-based FCBFormer model raised the mean Dice similarity coefficient from 0.814 to 0.870 and significantly reduced the mean L4–L5 segmental Cobb angle error from about 15.8° to 4.7°, a 70% improvement. The framework also improved intraclass correlation coefficients for key parameters, surpassing 0.70 for pelvic tilt, lumbar lordosis, and sacral slope.

By Sang-Jin Park, Jinyoung Choi, Seokwon Kim, Seungeon Song, Insu Park, Dougho Park, Taeyeon Kim, Youjin Lee, Donghoon Yang, Jaeman Cho, Joongwon Yang, Mansu Kim, Heumdai Kwon, Hong Gyu Baek, Dae Chul Cho, Injung Kim
arXiv Computer Vision
Sep 4

Hold-Out Self-Validation Cannot Certify Photogrammetric Accuracy: Saturation and Blindness to Coherent Distortion

The paper argues that internal self-consistency checks cannot guarantee the accuracy of photogrammetric reconstructions, a limitation that is structural rather than a tuning issue. It introduces a track‑leakage‑free hold‑out protocol that withholds a deterministic subset of images and tests each against only 3D points supported by at least two retained images, ensuring no view is evaluated against the structure it helped create. Experiments on diverse datasets show that while the protocol is well‑posed, it saturates at a confidence score of 1.00 and fails to detect coherent distortion, missing large errors that can reach over 100 m. whyItMatters":"The study highlights that hold‑out self‑validation scores, increasingly used as quality evidence for metric deliverables, may be misleading and cannot replace external survey validation."

By Behnam Asadi
arXiv Machine Learning
Sep 21

Detection is solved, delineation is not: what governs tooth segmentation on panoramic radiographs

The study evaluates automatic tooth segmentation on panoramic radiographs using a large annotated corpus of 1,422 images and 42,142 tooth polygons. It finds that increasing input resolution improves boundary precision (mask mAP50‑95 rises from 0.656 to 0.717) while detection performance remains unchanged, and that architectural changes have minimal impact on in‑domain accuracy. Targeted interventions such as LoRA adaptation, promptable foundation models, and anatomical label assignment provide negligible gains, indicating that resolution and acquisition diversity should be prioritized over model novelty.

By Muhammad Rehan, Moaz Amjad, Syed Danial Ahmed, Mariam Adnan, Haider Ali