# Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models

arXiv: https://arxiv.org/abs/2012.08721 · HTML: https://mlsanity.com/paper/2012.08721/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2020.

## Built on by (most cited first, 30 of 45)

- [Large-Vocabulary Segmentation for Medical Images with Text Prompts](https://mlsanity.com/paper/2312.17183/) (2023)
- [SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks](https://mlsanity.com/paper/2311.11969/) (2023)
- [DDGS-CT: Direction-Disentangled Gaussian Splatting for Realistic Volume Rendering](https://mlsanity.com/paper/2406.02518/) (2024)
- [MAISI-v2: Accelerated 3D High-Resolution Medical Image Synthesis with Rectified Flow and Region-specific Contrastive Loss](https://mlsanity.com/paper/2508.05772/) (2025)
- [Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2205.07471/) (2022)
- [Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines](https://mlsanity.com/paper/2307.13375/) (2023)
- [Rotation Equivariant Proximal Operator for Deep Unfolding Methods in Image Restoration](https://mlsanity.com/paper/2312.15701/) (2023)
- [Show and Segment: Universal Medical Image Segmentation via In-Context Learning](https://mlsanity.com/paper/2503.19359/) (2025)
- [MIMO: A medical vision language model with visual referring multimodal input and pixel grounding multimodal output](https://mlsanity.com/paper/2510.10011/) (2025)
- [Unsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains](https://mlsanity.com/paper/2308.16742/) (2023)
- [Universal Segmentation of 33 Anatomies](https://mlsanity.com/paper/2203.02098/) (2022)
- [DiffuX2CT: Diffusion Learning to Reconstruct CT Images from Biplanar X-Rays](https://mlsanity.com/paper/2407.13545/) (2024)
- [MedSG-Bench: A Benchmark for Medical Image Sequences Grounding](https://mlsanity.com/paper/2505.11852/) (2025)
- [Orientation-Shared Convolution Representation for CT Metal Artifact Learning](https://mlsanity.com/paper/2212.13166/) (2022)
- [MEPNet: A Model-Driven Equivariant Proximal Network for Joint Sparse-View Reconstruction and Metal Artifact Reduction in CT Images](https://mlsanity.com/paper/2306.14274/) (2023)
- [Slide-SAM: Medical SAM Meets Sliding Window](https://mlsanity.com/paper/2311.10121/) (2023)
- [RayEmb: Arbitrary Landmark Detection in X-Ray Images Using Ray Embedding Subspace](https://mlsanity.com/paper/2410.08152/) (2024)
- [PELE scores: Pelvic X-ray Landmark Detection by Pelvis Extraction and Enhancement](https://mlsanity.com/paper/2305.04294/) (2023)
- [Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge](https://mlsanity.com/paper/2504.02382/) (2025)
- [A Regularization-Guided Equivariant Approach for Image Restoration](https://mlsanity.com/paper/2505.19799/) (2025)
- [Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction](https://mlsanity.com/paper/2501.15610/) (2025)
- [Render-FM: Feedforward Model for Real-time Photorealistic Volumetric Rendering](https://mlsanity.com/paper/2505.17338/) (2025)
- [Zero-shot capability of SAM-family models for bone segmentation in CT scans](https://mlsanity.com/paper/2411.08629/) (2024)
- [Where is the disease? Semi-supervised pseudo-normality synthesis from an abnormal image](https://mlsanity.com/paper/2106.15345/) (2021)
- [Towards Cross-Scale Attention and Surface Supervision for Fractured Bone Segmentation in CT](https://mlsanity.com/paper/2405.01204/) (2024)
- [Boosting Segmentation Performance across datasets using histogram specification with application to pelvic bone segmentation](https://mlsanity.com/paper/2101.11135/) (2021)
- [Validation of musculoskeletal segmentation model with uncertainty estimation for bone and muscle assessment in hip-to-knee clinical CT images](https://mlsanity.com/paper/2409.02770/) (2024)
- [Learning Generalizable Features for Tibial Plateau Fracture Segmentation Using Masked Autoencoder and Limited Annotations](https://mlsanity.com/paper/2502.02862/) (2025)
- [SPIDER: Structure-Preferential Implicit Deep Network for Biplanar X-ray Reconstruction](https://mlsanity.com/paper/2507.04684/) (2025)
- [Learning Generalizable 3D Medical Image Representations from Mask-Guided Self-Supervision](https://mlsanity.com/paper/2603.13660/) (2026)

## Abstract (arXiv)

Purpose: Pelvic bone segmentation in CT has always been an essential step in clinical diagnosis and surgery planning of pelvic bone diseases. Existing methods for pelvic bone segmentation are either hand-crafted or semi-automatic and achieve limited accuracy when dealing with image appearance variations due to the multi-site domain shift, the presence of contrasted vessels, coprolith and chyme, bone fractures, low dose, metal artifacts, etc. Due to the lack of a large-scale pelvic CT dataset with annotations, deep learning methods are not fully explored. Methods: In this paper, we aim to bridge the data gap by curating a large pelvic CT dataset pooled from multiple sources and different manufacturers, including 1, 184 CT volumes and over 320, 000 slices with different resolutions and a variety of the above-mentioned appearance variations. Then we propose for the first time, to the best of our knowledge, to learn a deep multi-class network for segmenting lumbar spine, sacrum, left hip, and right hip, from multiple-domain images simultaneously to obtain more effective and robust feature representations. Finally, we introduce a post-processing tool based on the signed distance function (SDF) to eliminate false predictions while retaining correctly predicted bone fragments. Results: Extensive experiments on our dataset demonstrate the effectiveness of our automatic method, achieving an average Dice of 0.987 for a metal-free volume. SDF post-processor yields a decrease of 10.5% in hausdorff distance by maintaining important bone fragments in post-processing phase. Conclusion: We believe this large-scale dataset will promote the development of the whole community and plan to open source the images, annotations, codes, and trained baseline models at https://github.com/ICT-MIRACLE-lab/CTPelvic1K.