Two-Stage Structure-Focused Contrastive Learning for Automatic Identification and Localization of Complex Pelvic Fractures

Automatic pelvic fracture segmentation: a deep learning approach and benchmark dataset

Introduction

Accurate segmentation of pelvic fractures from computed tomography (CT) is crucial for trauma diagnosis and image-guided reduction surgery. The traditional manual slice-by-slice segmentation by surgeons is time-consuming, experience-dependent, and error-prone. The complex anatomy of the pelvic bone, the diversity of fracture types, and the variability in fracture surface appearances pose significant challenges to automated solutions.

Methods

We propose an automatic pelvic fracture segmentation method based on deep learning, which effectively isolates hipbone and sacrum fragments from fractured pelvic CT. The method employs two sequential networks: an anatomical segmentation network for extracting hipbones and sacrum from CT images, followed by a fracture segmentation network that isolates the main and minor fragments within each bone region. We propose a distance-weighted loss to guide the fracture segmentation network's attention on the fracture surface. Additionally, multi-scale deep supervision and smooth transition strategies are incorporated to enhance overall performance.

Results

Tested on a curated dataset of 150 CTs, which we have made publicly available, our method achieves an average Dice coefficient of 0.986 and an average symmetric surface distance of 0.234 mm.

Discussion

The method outperformed traditional max-flow and a transformer-based method, demonstrating its effectiveness in handling complex fracture.

Interpretable Severity Scoring of Pelvic Trauma Through Automated Fracture Detection and Bayesian Inference

Pelvic ring disruptions result from blunt injury mechanisms and are potentially lethal mainly due to associated injuries and massive pelvic hemorrhage. The severity of pelvic fractures in trauma victims is frequently assessed by grading the fracture according to the Tile AO/OTA classification in whole-body Computed Tomography (CT) scans. Due to the high volume of whole-body CT scans generated in trauma centers, the overall information content of a single whole-body CT scan and low manual CT reading speed, an automatic approach to Tile classification would provide substantial value, e.g., to prioritize the reading sequence of the trauma radiologists or enable them to focus on other major injuries in multi-trauma patients. In such a high-stakes scenario, an automated method for Tile grading should ideally be transparent such that the symbolic information provided by the method follows the same logic a radiologist or orthopedic surgeon would use to determine the fracture grade. This paper introduces an automated yet interpretable pelvic trauma decision support system to assist radiologists in fracture detection and Tile grading. To achieve interpretability despite processing high-dimensional whole-body CT images, we design a neurosymbolic algorithm that operates similarly to human interpretation of CT scans. The algorithm first detects relevant pelvic fractures on CTs with high specificity using Faster-RCNN. To generate robust fracture detections and associated detection (un)certainties, we perform test-time augmentation of the CT scans to apply fracture detection several times in a self-ensembling approach. The fracture detections are interpreted using a structural causal model based on clinical best practices to infer an initial Tile grade. We apply a Bayesian causal model to recover likely co-occurring fractures that may have been rejected initially due to the highly specific operating point of the detector, resulting in an updated list of detected fractures and corresponding final Tile grade. Our method is transparent in that it provides fracture location and types, as well as information on important counterfactuals that would invalidate the system's recommendation. Our approach achieves an AUC of 0.89/0.74 for translational and rotational instability,which is comparable to radiologist performance. Despite being designed for human-machine teaming, our approach does not compromise on performance compared to previous black-box methods.

An End-to-End Geometry-Based Pipeline for Automatic Preoperative Surgical Planning of Pelvic Fracture Reduction and Fixation

Computer-assisted preoperative planning of pelvic fracture reduction surgery has the potential to increase the accuracy of the surgery and to reduce complications. However, the diversity of the pelvic fractures and the disturbance of small fracture fragments present a great challenge to perform reliable automatic preoperative planning. In this paper, we present a comprehensive and automatic preoperative planning pipeline for pelvic fracture surgery. It includes pelvic fracture labeling, reduction planning of the fracture, and customized screw implantation. First, automatic bone fracture labeling is performed based on the separation of the fracture sections. Then, fracture reduction planning is performed based on automatic extraction and pairing of the fracture surfaces. Finally, screw implantation is planned using the adjoint fracture surfaces. The proposed pipeline was tested on different types of pelvic fracture in 14 clinical cases. Our method achieved a translational and rotational accuracy of 2.56 mm and 3.31° in reduction planning. For fixation planning, a clinical acceptance rate of 86.7% was achieved. The results demonstrate the feasibility of the clinical application of our method. Our method has shown accuracy and reliability for complex multi-body bone fractures, which may provide effective clinical preoperative guidance and may improve the accuracy of pelvic fracture reduction surgery.

A bidirectional framework for fracture simulation and deformation-based restoration prediction in pelvic fracture surgical planning

Pelvic fracture is a severe trauma with life-threatening implications. Surgical reduction is essential for restoring the anatomical structure and functional integrity of the pelvis, requiring accurate preoperative planning. However, the complexity of pelvic fractures and limited data availability necessitate labor-intensive manual corrections in a clinical setting. We describe in this paper a novel bidirectional framework for automatic pelvic fracture surgical planning based on fracture simulation and structure restoration. Our fracture simulation method accounts for patient-specific pelvic structures, bone density information, and the randomness of fractures, enabling the generation of various types of fracture cases from healthy pelvises. Based on these features and on adversarial learning, we develop a novel structure restoration network to predict the deformation mapping in CT images before and after a fracture for the precise structural reconstruction of any fracture. Furthermore, a self-supervised strategy based on pelvic anatomical symmetry priors is developed to optimize the details of the restored pelvic structure. Finally, the restored pelvis is used as a template to generate a surgical reduction plan in which the fragments are repositioned in an efficient jigsaw puzzle registration manner. Extensive experiments on simulated and clinical datasets, including scans with metal artifacts, show that our method achieves good accuracy and robustness: a mean SSIM of 90.7% for restorations, with translational errors of 2.88 mm and rotational errors of 3.18°for reductions in real datasets. Our method takes 52.9 s to complete the surgical planning in the phantom study, representing a significant acceleration compared to standard clinical workflows. Our method may facilitate effective surgical planning for pelvic fractures tailored to individual patients in clinical settings.

Better Rough Than Scarce: Proximal Femur Fracture Segmentation With Rough Annotations

Proximal femoral fracture segmentation in computed tomography (CT) is essential in the preoperative planning of orthopedic surgeons. Recently, numerous deep learning-based approaches have been proposed for segmenting various structures within CT scans. Nevertheless, distinguishing various attributes between fracture fragments and soft tissue regions in CT scans frequently poses challenges, which have received comparatively limited research attention. Besides, the cornerstone of contemporary deep learning methodologies is the availability of annotated data, while detailed CT annotations remain scarce. To address the challenge, we propose a novel weakly-supervised framework, namely Rough Turbo Net (RT-Net), for the segmentation of proximal femoral fractures. We emphasize the utilization of human resources to produce rough annotations on a substantial scale, as opposed to relying on limited fine-grained annotations that demand a substantial time to create. In RT-Net, rough annotations pose fractured-region constraints, which have demonstrated significant efficacy in enhancing the accuracy of the network. Conversely, the fine annotations can provide more details for recognizing edges and soft tissues. Besides, we design a spatial adaptive attention module (SAAM) that adapts to the spatial distribution of the fracture regions and align feature in each decoder. Moreover, we propose a fine-edge loss which is applied through an edge discrimination network to penalize the absence or imprecision edge features. Extensive quantitative and qualitative experiments demonstrate the superiority of RT-Net to state-of-the-art approaches. Furthermore, additional experiments show that RT-Net has the capability to produce pseudo labels for raw CT images that can further improve fracture segmentation performance and has the potential to improve segmentation performance on public datasets. The code is available at: https://github.com/zyairelu/RT-Net.

Fragment distance-guided dual-stream learning for automatic pelvic fracture segmentation

Pelvic fracture is a complex and severe injury. Accurate diagnosis and treatment planning require the segmentation of the pelvic structure and the fractured fragments from preoperative CT scans. However, this segmentation is a challenging task, as the fragments from a pelvic fracture typically exhibit considerable variability and irregularity in the morphologies, locations, and quantities. In this study, we propose a novel dual-stream learning framework for the automatic segmentation and category labeling of pelvic fractures. Our method uniquely identifies pelvic fracture fragments in various quantities and locations using a dual-branch architecture that leverages distance learning from bone fragments. Moreover, we develop a multi-size feature fusion module that adaptively aggregates features from diverse receptive fields tailored to targets of different sizes and shapes, thus boosting segmentation performance. Extensive experiments on three pelvic fracture datasets from different medical centers demonstrated the accuracy and generalizability of the proposed method. It achieves a mean Dice coefficient and mean Sensitivity of 0.935±0.068 and 0.929±0.058 in the dataset FracCLINIC, and 0.955±0.072 and 0.912±0.125 in the dataset FracSegData, which are superior than other comparing methods. Our method optimizes the process of pelvic fracture segmentation, potentially serving as an effective tool for preoperative planning in the clinical management of pelvic fractures.

M3YOLOv5: Feature enhanced YOLOv5 model for mandibular fracture detection

BACKGROUND

It is very important to detect mandibular fracture region. However, the size of mandibular fracture region is different due to different anatomical positions, different sites and different degrees of force. It is difficult to locate and recognize fracture region accurately.

METHODS

To solve these problems, M3YOLOv5 model is proposed in this paper. Three feature enhancement strategies are designed, which improve the ability of model to locate and recognize mandibular fracture region. Firstly, Global-Local Feature Extraction Module (GLFEM) is designed. By effectively combining Convolutional Neural Network (CNN) and Transformer, the problem of insufficient global information extraction ability of CNN is complemented, and the positioning ability of the model to the fracture region is improved. Secondly, in order to improve the interaction ability of context information, Deep-Shallow Feature Interaction Module (DSFIM) is designed. In this module, the spatial information in the shallow feature layer is embedded to the deep feature layer by the spatial attention mechanism, and the semantic information in the deep feature layer is embedded to the shallow feature layer by the channel attention mechanism. The fracture region recognition ability of the model is improved. Finally, Multi-scale Multi receptive-field Feature Mixing Module (MMFMM) is designed. Deep separate convolution chains are used in this modal, which is composed by multiple layers of different scales and different dilation coefficients. This method provides richer receptive field for the model, and the ability to detect fracture region of different scales is improved.

RESULTS

The precision rate, mAP value, recall rate and F1 value of M3YOLOv5 model on mandibular fracture CT data set are 97.18%, 96.86%, 94.42% and 95.58% respectively. The experimental results show that there is better performance about M3YOLOv5 model than the mainstream detection models.

CONCLUSION

The M3YOLOv5 model can effectively recognize and locate the mandibular fracture region, which is of great significance for doctors' clinical diagnosis.

Exploring Fracture Patterns: Assessing Representation Methods for Bone Fracture Simulation

Fracture pattern acquisition and representation in human bones play a crucial role in medical simulation, diagnostics, and treatment planning. This article presents a comprehensive review of methodologies employed in acquiring and representing bone fracture patterns. Several techniques, including segmentation algorithms, curvature analysis, and deep learning-based approaches, are reviewed to determine their effectiveness in accurately identifying fracture zones. Additionally, diverse methods for representing fracture patterns are evaluated. The challenges inherent in detecting accurate fracture zones from medical images, the complexities arising from multifragmentary fractures, and the need to automate fracture reduction processes are elucidated. A detailed analysis of the suitability of each representation method for specific medical applications, such as simulation systems, surgical interventions, and educational purposes, is provided. The study explores insights from a broad spectrum of research articles, encompassing diverse methodologies and perspectives. This review elucidates potential directions for future research and contributes to advancements in comprehending the acquisition and representation of fracture patterns in human bone.