The segmentation of pelvic fracture fragments in CT and X-ray images is crucial for trauma diagnosis, surgical planning, and intraoperative guidance. However, accurately and efficiently delineating the bone fragments remains a significant challenge due to complex anatomy and imaging limitations. The PENGWIN challenge, organized as a MICCAI 2024 satellite event, aimed to advance automated fracture segmentation by benchmarking state-of-the-art algorithms on these complex tasks. A diverse dataset of 150 CT scans was collected from multiple clinical centers, and a large set of simulated X-ray images was generated using the DeepDRR method. Final submissions from 16 teams worldwide were evaluated under a rigorous multi-metric testing scheme. The top-performing CT algorithm achieved an average fragment-wise intersection over union (IoU) of 0.930, demonstrating satisfactory accuracy. However, in the X-ray task, the best algorithm achieved an IoU of 0.774, which is promising but not yet sufficient for intra-operative decision-making, reflecting the inherent challenges of fragment overlap in projection imaging. Beyond the quantitative evaluation, the challenge revealed methodological diversity in algorithm design. Variations in instance representation, such as primary-secondary classification versus boundary-core separation, led to differing segmentation strategies. Despite promising results, the challenge also exposed inherent uncertainties in fragment definition, particularly in cases of incomplete fractures. These findings suggest that interactive segmentation approaches, integrating human decision-making with task-relevant information, may be essential for improving model reliability and clinical applicability.

Pelvic fractures are among the most complex challenges in orthopedic trauma, which usually involve hipbone and sacrum fractures, as well as joint dislocations. Traditional preoperative surgical planning relies on the operator's subjective interpretation of CT images, which is both time-consuming and prone to inaccuracies. This study introduces an automated preoperative planning solution for pelvic fracture reduction, addressing the limitations of conventional methods. The proposed solution includes a novel multi-scale distance-weighted neural network for segmenting pelvic fracture fragments from CT scans, and a learning-based approach to restore pelvic structure, combining a morphable model-based method for single-bone fracture reduction and a recursive pose estimation module for joint dislocation reduction. Comprehensive experiments on a clinical dataset of 30 fracture cases demonstrated the efficacy of our methods. Our segmentation network outperformed traditional max-flow segmentation and networks without distance weighting, achieving a Dice similarity coefficient (DSC) of 0.986 ± 0.055 and a local DSC of 0.940 ± 0.056 around the fracture sites. The proposed reduction method surpassed mirroring and mean template techniques, and an optimization-based joint matching method, achieving a target reduction error of (3.265 ± 1.485) mm, rotation errors of (3.476 ± 1.995)°, and translation errors of (2.773 ± 1.390) mm. In the proof-of-concept cadaver studies, our method achieved a DSC of 0.988 in segmentation and 3.731 mm error in reduction planning, which senior experts deemed excellent. In conclusion, our automated approach significantly improves traditional preoperative planning, enhancing both efficiency and accuracy in pelvic fracture reduction.

Accurate orthopedic fracture reduction planning is essential for ensuring successful postoperative recovery and improving patient outcomes. However, current automatic methods are challenged by the complex and irregular fracture geometries and the scarcity of annotated training data. To address these challenges, we propose a novel approach that integrates learning-based shape restoration and fracture simulation. A transformer-based model is developed, which utilizes patch-to-patch shape translation and recursive fragment registration to iteratively refine fracture reduction poses. A deformable fracture generation model (DFGM) combines statistical shape modeling with clinically representative fracture patterns to generate diverse and realistic datasets, reducing the dependence on annotated samples. Tested on extensive clinical data with hipbone, sacrum, and femoral shaft fractures, the proposed method achieved mean errors of 1.85 mm and 3.40°, outperforming both template-based and existing learning-based methods. In addition, models trained solely on DFGM-synthesized data presented strong generalizability to real clinical data. The ablation experiments demonstrate the effectiveness of the fragment-aware network pipeline and the synthesis steps. Finally, a cadaver study with ground truth derived from the pre-injury scan further validated the performance of the method.

Untreated pain in critically ill patients can lead to immunosuppression and increased metabolic activity, with severe clinical consequences such as tachypnea and delirium. Continuous pain assessment is challenging due to nursing shortages and intensive care unit (ICU) workload. Mechanical ventilation equipment obscures the facial features of many patients in the ICU, making previous facial pain detection methods based on full-face images inapplicable. This paper proposes a facial Action Units (AUs) guided pain assessment network for faces under occlusion. The network consists of an AU-guided (AUG) module, a texture feature extraction (TFE) module, and a pain assessment (PA) module. The AUG module automatically detects AUs in the non-occluded areas of the face. In contrast, the TFE module detects the facial landmarks and crops prior knowledge patches, a random exploration patch, and a global feature patch. Then these patches are fed into two convolutional networks to extract texture features. Afterward, the designed AU guidances and texture features are fused in the PA module to assess the pain state. Extensive validation is conducted on a public dataset and two datasets created in this work. The proposed network architecture achieves superior performance in binary classification, four-class classification, and intensity regression tasks. In addition, we have successfully applied the network to actual data collected in the laboratory environment with excellent results.

Pelvic fracture is a serious high-energy injury with highest disability and mortality rate among all fractures. At present, the reduction of pelvic fracture is still completely dependent on surgeons' experience, which may lead to poor effect of pelvic reduction, thus seriously affecting surgical treatment and postoperative rehabilitation of patients. Based on this, a new robotic system for pelvic fracture reduction was developed and tested. Withdrawing on the optical tracking system, the real-time 3D navigation of pelvic position during operation was realized through Nonrigid ICP method. The target position for fracture reduction was obtained through pelvic symmetry reduction method based on structural symmetry of the pelvis. The shortest reduction path was planned automatically, which could be adjusted by surgeons manually. Finally, the pelvic fracture reduction operation was completed through the robot. System accuracy and effectiveness were demonstrated through laboratory trials and preliminary cadaveric trials. The system resulted in high fracture reduction reliability with the registration accuracy of 1.3749 ± 0.6311mm, and the robot reduction accuracy of 2.8925 ± 0.8647mm. Preliminary cadaveric trials also provided a positive and favorable outcome pointing to the usability of the system in the operating theatre, potentially enhancing the capacity of pelvic fracture surgeries.