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 .

中文翻译：

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粗略比稀缺好：带有粗略注释的股骨近端骨折分割


计算机断层扫描 （CT） 中的股骨近端骨折分割在骨科医生的术前计划中至关重要。最近，已经提出了许多基于深度学习的方法，用于在 CT 扫描中分割各种结构。然而，在 CT 扫描中区分骨折碎片和软组织区域之间的各种属性经常带来挑战，这些挑战受到的研究关注相对有限。此外，当代深度学习方法的基石是注释数据的可用性，而详细的 CT 注释仍然稀缺。为了应对这一挑战，我们提出了一种新的弱监督框架，即 Rough Turbo Net （RT-Net），用于分割股骨近端骨折。我们强调利用人力资源来大规模生成粗略的注释，而不是依赖需要大量时间来创建的有限的细粒度注释。在 RT-Net 中，粗略注释会带来断裂区域约束，这在提高网络准确性方面已显示出显着的有效性。相反，精细注释可以为识别边缘和软组织提供更多细节。此外，我们设计了一个空间自适应注意力模块 （SAAM），以适应每个解码器中骨折区域的空间分布和对齐特征。此外，我们提出了一种细边缘损失，通过边缘判别网络来惩罚缺失或不精确的边缘特征。广泛的定量和定性实验证明了 RT-Net 优于最先进的方法。 此外，其他实验表明，RT-Net 能够为原始 CT 图像生成伪标签，这可以进一步提高骨折分割性能，并有可能提高公共数据集的分割性能。该代码可在以下网址获得： https://github.com/zyairelu/RT-Net 。