# Class-Aware Cartilage Segmentation for Autonomous US-CT Registration in Robotic Intercostal Ultrasound Imaging

arXiv: https://arxiv.org/abs/2406.04100 · HTML: https://mlsanity.com/paper/2406.04100/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 71 references that are in the map.
- First posted on arXiv 6 Jun 2024.

## Builds on (most cited first, 25 of 25)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Boundary loss for highly unbalanced segmentation](https://mlsanity.com/paper/1812.07032/) (2018)
- [Cardiac Segmentation with Strong Anatomical Guarantees](https://mlsanity.com/paper/2006.08825/) (2020)
- [Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation](https://mlsanity.com/paper/2104.05239/) (2021)
- [Class-Aware Adversarial Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2201.10737/) (2022)
- [A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification](https://mlsanity.com/paper/1903.03313/) (2019)
- [Robotic Ultrasound Imaging: State-of-the-Art and Future Perspectives](https://mlsanity.com/paper/2307.05545/) (2023)
- [Towards MRI-Based Autonomous Robotic US Acquisitions: A First Feasibility Study](https://mlsanity.com/paper/1607.08371/) (2016)
- [Autonomous Robotic Screening of Tubular Structures based only on Real-Time Ultrasound Imaging Feedback](https://mlsanity.com/paper/2011.00099/) (2020)
- [Machine Learning in Robotic Ultrasound Imaging: Challenges and Perspectives](https://mlsanity.com/paper/2401.02376/) (2024)
- [Deformation-Aware Robotic 3D Ultrasound](https://mlsanity.com/paper/2107.08411/) (2021)
- [Intelligent Robotic Sonographer: Mutual Information-based Disentangled Reward Learning from Few Demonstrations](https://mlsanity.com/paper/2307.03705/) (2023)
- [Towards Autonomous Atlas-based Ultrasound Acquisitions in Presence of Articulated Motion](https://mlsanity.com/paper/2208.05399/) (2022)
- [Motion-Aware Robotic 3D Ultrasound](https://mlsanity.com/paper/2107.05998/) (2021)
- [Precise Repositioning of Robotic Ultrasound: Improving Registration-based Motion Compensation using Ultrasound Confidence Optimization](https://mlsanity.com/paper/2208.05383/) (2022)
- [Skeleton Graph-based Ultrasound-CT Non-rigid Registration](https://mlsanity.com/paper/2305.08228/) (2023)
- [DefCor-Net: Physics-Aware Ultrasound Deformation Correction](https://mlsanity.com/paper/2308.03865/) (2023)
- [Thoracic Cartilage Ultrasound-CT Registration using Dense Skeleton Graph](https://mlsanity.com/paper/2307.03800/) (2023)
- [Autonomous Path Planning for Intercostal Robotic Ultrasound Imaging Using Reinforcement Learning](https://mlsanity.com/paper/2404.09927/) (2024)
- [Orientation-guided Graph Convolutional Network for Bone Surface Segmentation](https://mlsanity.com/paper/2206.08481/) (2022)

## Built on by (most cited first, 5 of 5)

- [Speckle2Self: Self-Supervised Ultrasound Speckle Reduction Without Clean Data](https://mlsanity.com/paper/2507.06828/) (2025)
- [UltraAD: Fine-Grained Ultrasound Anomaly Classification via Few-Shot CLIP Adaptation](https://mlsanity.com/paper/2506.19694/) (2025)
- [Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Medical Images](https://mlsanity.com/paper/2411.04004/) (2024)
- [Scanning While Imagining: A Scene-Graph World Model for Robotic Ultrasound Navigation](https://mlsanity.com/paper/2609.32837/) (2026)
- [Mask2Restore: Self-Supervised Ultrasound Despeckling via Inpainting](https://mlsanity.com/paper/2609.32844/) (2026)

## Abstract (arXiv)

Ultrasound imaging has been widely used in clinical examinations owing to the advantages of being portable, real-time, and radiation-free. Considering the potential of extensive deployment of autonomous examination systems in hospitals, robotic US imaging has attracted increased attention. However, due to the inter-patient variations, it is still challenging to have an optimal path for each patient, particularly for thoracic applications with limited acoustic windows, e.g., intercostal liver imaging. To address this problem, a class-aware cartilage bone segmentation network with geometry-constraint post-processing is presented to capture patient-specific rib skeletons. Then, a dense skeleton graph-based non-rigid registration is presented to map the intercostal scanning path from a generic template to individual patients. By explicitly considering the high-acoustic impedance bone structures, the transferred scanning path can be precisely located in the intercostal space, enhancing the visibility of internal organs by reducing the acoustic shadow. To evaluate the proposed approach, the final path mapping performance is validated on five distinct CTs and two volunteer US data, resulting in ten pairs of CT-US combinations. Results demonstrate that the proposed graph-based registration method can robustly and precisely map the path from CT template to individual patients (Euclidean error: 2.21±1.11~mm).