Lumbar vertebral body cancellous bone location and segmentation is crucial in an automated lumbar spine processing pipeline. Accurate and reliable analysis of lumbar spine image is expected to advantage practical medical diagnosis and population-based analysis of bone strength. However, the design of automated algorithms for lumbar spine processing is demanding due to significant anatomical variations and scarcity of publicly available data. In recent years, convolutional neural network (CNN) and vision transformers (Vits) have been the de facto standard in medical image segmentation. Although adept at capturing global features, the inherent bias of locality and weight sharing of CNN constrains its capacity to model long-range dependency. In contrast, Vits excel at long-range dependency modeling, but they may not generalize well with limited datasets due to the lack of inductive biases inherent to CNN. In this paper, we propose a deep learning-based two-stage coarse-to-fine solution to address the problem of automatic location and segmentation of lumbar vertebral body cancellous bone. Specifically, in the first stage, a Swin-transformer based model is applied to predict the heatmap of lumbar vertebral body centroids. Considering the characteristic anatomical structure of lumbar spine, we propose a novel loss function called LumAnatomy loss, which enforces the order and bend of the predicted vertebral body centroids. To inherit the excellence of CNN and Vits while preventing their respective limitations, in the second stage, we propose an encoder–decoder network to segment the identified lumbar vertebral body cancellous bone, which consists of two parallel encoders, i.e., a Swin-transformer encoder and a CNN encoder. To enhance the combination of CNNs and Vits, we propose a novel multi-scale attention feature fusion module (MSA-FFM), which address issues that arise when fusing features given at different encoders. To tackle the issue of lack of data, we raise the first large-scale lumbar vertebral body cancellous bone segmentation dataset called LumVBCanSeg containing a total of 185 CT scans annotated at voxel level by 3 physicians. Extensive experimental results on the LumVBCanSeg dataset demonstrate the proposed algorithm outperform other state-of-the-art medical image segmentation methods. The data is publicly available at: . The implementation of the proposed method is available at: .

中文翻译：

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LumVertCancNet：一种基于混合Swin-transformer的新型3D腰椎体松质骨定位和分割方法

腰椎椎体松质骨的定位和分割在自动化腰椎处理流程中至关重要。腰椎图像的准确可靠的分析有望有利于实际的医学诊断和基于人群的骨强度分析。然而，由于显着的解剖变化和公开数据的稀缺，腰椎处理的自动化算法的设计要求很高。近年来，卷积神经网络（CNN）和视觉变换器（Vits）已成为医学图像分割的事实上的标准。尽管擅长捕获全局特征，但 CNN 的局部性和权重共享的固有偏差限制了其模拟远程依赖性的能力。相比之下，Vits 擅长远程依赖建模，但由于缺乏 CNN 固有的归纳偏差，它们可能无法在有限的数据集上很好地泛化。在本文中，我们提出了一种基于深度学习的两阶段从粗到精的解决方案来解决腰椎体松质骨的自动定位和分割问题。具体来说，在第一阶段，应用基于Swin-transformer的模型来预测腰椎椎体质心的热图。考虑到腰椎的特征解剖结构，我们提出了一种称为 LumAnatomy 损失的新型损失函数，它强制预测椎体质心的顺序和弯曲。为了继承CNN和Vits的优点，同时避免各自的局限性，在第二阶段，我们提出了一种编码器-解码器网络来分割识别的腰椎体松质骨，该网络由两个并行编码器组成，即Swin-transformer编码器和 CNN 编码器。为了增强 CNN 和 Vits 的结合，我们提出了一种新颖的多尺度注意力特征融合模块（MSA-FFM），它解决了融合不同编码器给出的特征时出现的问题。为了解决数据缺乏的问题，我们提出了第一个大规模腰椎体松质骨分割数据集 LumVBCanSeg，其中包含由 3 名医生在体素级别注释的总共 185 个 CT 扫描。 LumVBCanSeg 数据集上的大量实验结果表明，所提出的算法优于其他最先进的医学图像分割方法。该数据可在以下网址公开获取： 。所提出方法的实施可在以下位置获得： 。