# Towards Cross-Scale Attention and Surface Supervision for Fractured Bone Segmentation in CT

arXiv: https://arxiv.org/abs/2405.01204 · HTML: https://mlsanity.com/paper/2405.01204/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 2 May 2024.

## Builds on (most cited first, 12 of 12)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [Boundary loss for highly unbalanced segmentation](https://mlsanity.com/paper/1812.07032/) (2018)
- [Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models](https://mlsanity.com/paper/2012.08721/) (2020)
- [Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound](https://mlsanity.com/paper/1907.01743/) (2019)
- [Non-Iterative Scribble-Supervised Learning with Pacing Pseudo-Masks for Medical Image Segmentation](https://mlsanity.com/paper/2210.10956/) (2022)
- [SimPLe: Similarity-Aware Propagation Learning for Weakly-Supervised Breast Cancer Segmentation in DCE-MRI](https://mlsanity.com/paper/2306.16714/) (2023)
- [Recurrent Feature Propagation and Edge Skip-Connections for Automatic Abdominal Organ Segmentation](https://mlsanity.com/paper/2201.00317/) (2022)

## Built on by (most cited first, 2 of 2)

- [Learning Generalizable Features for Tibial Plateau Fracture Segmentation Using Masked Autoencoder and Limited Annotations](https://mlsanity.com/paper/2502.02862/) (2025)
- [MedGEN-Bench: A Contextually Entangled Benchmark for Open-ended Multimodal Medical Generation](https://mlsanity.com/paper/2511.13135/) (2025)

## Abstract (arXiv)

Bone segmentation is an essential step for the preoperative planning of fracture trauma surgery. The automated segmentation of fractured bone from computed tomography (CT) scans remains challenging, due to the large differences of fractures in position and morphology, and also the inherent anatomical characteristics of different bone structures. To alleviate these issues, we propose a cross-scale attention mechanism as well as a surface supervision strategy for fractured bone segmentation in CT. Specifically, a cross-scale attention mechanism is introduced to effectively aggregate the features among different scales to provide more powerful fracture representation. Moreover, a surface supervision strategy is employed, which explicitly constrains the network to pay more attention to the bone boundary. The efficacy of the proposed method is evaluated on a public dataset containing CT scans with hip fractures. The evaluation metrics are Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and Hausdorff distance (95HD). The proposed method achieves an average DSC of 93.36%, ASSD of 0.85mm, 95HD of 7.51mm. Our method offers an effective fracture segmentation approach for the pelvic CT examinations, and has the potential to be used for improving the segmentation performance of other types of fractures.