Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China; Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China; Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Comput Biol Med. 2024 Mar;171:108237. doi: 10.1016/j.compbiomed.2024.108237. Epub 2024 Feb 28.
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: https://zenodo.org/record/8181250. The implementation of the proposed method is available at: https://github.com/sia405yd/LumVertCancNet.
腰椎椎体松质骨的定位和分割在自动化腰椎处理管道中至关重要。预计对腰椎图像进行准确可靠的分析将有利于实际的医学诊断和基于人群的骨强度分析。然而，由于解剖结构变化显著，并且公开可用的数据稀缺，因此对自动化腰椎处理算法的设计要求很高。近年来，卷积神经网络（CNN）和视觉转换器（Vits）已成为医学图像分割的事实上的标准。虽然擅长捕捉全局特征，但 CNN 的局部性和权重共享的固有偏差限制了其建模长程依赖的能力。相比之下，Vits 在长程依赖建模方面表现出色，但由于缺乏 CNN 固有的归纳偏差，它们可能无法很好地推广到有限的数据集。在本文中，我们提出了一种基于深度学习的两阶段粗到精解决方案，以解决自动定位和分割腰椎椎体松质骨的问题。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中心点的热图。考虑到腰椎的解剖结构特征，我们提出了一种新的损失函数，称为 LumAnatomy 损失，它强制预测的椎体中心点的顺序和弯曲。为了继承 CNN 和 Vits 的优点，同时防止它们各自的局限性，在第二阶段，我们提出了一个编码器-解码器网络来分割所识别的腰椎椎体松质骨，它由两个平行的编码器组成，即 Swin-Transformer 编码器和 CNN 编码器。为了增强 CNN 和 Vits 的组合，我们提出了一种新的多尺度注意力特征融合模块（MSA-FFM），该模块解决了当融合来自不同编码器的特征时出现的问题。为了解决数据不足的问题，我们提出了第一个大规模的腰椎椎体松质骨分割数据集，称为 LumVBCanSeg，该数据集总共包含 185 个 CT 扫描，由 3 名医生进行了体素级注释。在 LumVBCanSeg 数据集上进行的广泛实验结果表明，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：https://zenodo.org/record/8181250 处获取。所提出方法的实现可在：https://github.com/sia405yd/LumVertCancNet 处获取。
