arXiv AI

BoneAgeTW2: Automated Skeletal Maturation Assessment via the Tanner-Whitehouse 2 Method, Deep Learning, and Clinical Report Generation with Distribution Curves

arXiv:2607. 23224v1 Announce Type: cross Abstract: We present BoneAgeTW2, the first fully open-source system to automate the complete Tanner-Whitehouse 2 (TW2) clinical protocol for skeletal maturity assessment end-to-end.

arXiv Machine Learning
Sep 16

RAM-H1200: A Unified Evaluation and Dataset on Hand Radiographs for Rheumatoid Arthritis

RAM‑H1200 is a new public benchmark comprising 1,200 hand radiographs from six medical centers, each annotated with whole‑hand bone instance segmentation, pixel‑level bone erosion masks, joint regions of interest, and joint‑level Sharp‑van der Heijde (SvdH) scores for bone erosion and joint space narrowing. The dataset enables unified multi‑level analysis of anatomical structure, localized erosive pathology, and clinically standardized RA severity, filling a gap in existing resources that lack full‑hand coverage and fine‑grained annotations. Initial benchmark results show strong performance in bone segmentation but highlight that bone erosion segmentation remains a significant challenge, underscoring the dataset’s potential to advance quantitative RA analysis.

By Songxiao Yang, Haolin Wang, Yao Fu, Junmu Peng, Lin Fan, Hongruixuan Chen, Jian Song, Masayuki Ikebe, Shinya Takamaeda-Yamazaki, Masatoshi Okutomi, Tamotsu Kamishima, Yafei Ou
arXiv Computer Vision
Aug 28

UniFLM: United Segmentation and Measurement on Fetal Limb Ultrasonic Image

The paper introduces UniFLM, a unified framework for segmenting and measuring fetal long bones in ultrasound images. It presents the Fetal Limb Bones (FLB) dataset with high‑quality annotations for the humerus, femur, tibia‑fibula, and radius‑ulna. UniFLM employs a Semantic‑Aware Skip Connection, a Positive Sampling strategy, and a Point Regression Mapping module to improve segmentation accuracy and bone length measurement, achieving superior performance over existing models on the FLB dataset.

By Zeen Zhou, Qiuhua Chen, Xiaojun Cao, Changmao Chen, Chao Sun, Bo Du
arXiv AI
Sep 16

Beyond In-Distribution Metrics: A Systematic Out-of-Distribution Evaluation of Congenital Heart Disease Segmentation

The paper presents the first systematic evaluation of out‑of‑distribution generalization for congenital heart disease (CHD) segmentation, using the ImageCHD cohort as a held‑out target. It compares several segmentation architectures under different training regimes, showing that in‑distribution performance is a poor predictor of cross‑cohort robustness: nnU‑Net drops from 0.77 to 0.51 Dice, while SwinUNETR maintains higher performance at 0.67 Dice. Limited target‑domain adaptation with only 11 labeled ImageCHD cases boosts all SwinUNETR variants above 0.76 Dice, highlighting the importance of explicit cross‑dataset testing.

By Aniketh Vijesh, Shrisharanyan Vasu, Abhijit Ramesh, Clare Pomeroy-Ward, Harikrishnan Anil Maya, Sarin Xavier, Mahesh Kappanayil, Gilad Gressel
arXiv Computer Vision
Sep 25

Integrating Local Detail and Global Context: A Dual-Input Multi-Task Learning Framework for Bone Tumor Diagnosis

The paper introduces a dual‑input, multi‑task learning framework that jointly segments and classifies bone tumors by applying bidirectional cross‑modal attention between a lesion crop and the full radiograph. Using a YOLO‑based detector and a dual‑stream DenseNet121 architecture, the model fuses fine‑grained lesion detail with global anatomical context through a novel cross‑modal attention fusion strategy and hierarchical multi‑scale feature fusion. On the multi‑institutional Bone Tumor X‑ray Radiograph Dataset, the approach outperforms single‑input baselines, achieving a Dice coefficient of 0.896 and a macro‑averaged F1‑score of 0.928, with an AUC of 0.999 for malignant osteosarcoma.

By S. M. Nasif Uddin, Rusab Sarmun, Muhammad E. H. Chowdhury, Adam Mushtak, Israa Al-Hashimi, Sohaib Bassam Zoghoul