IEEE Trans Med Imaging. 2023 Dec;42(12):3590-3601. doi: 10.1109/TMI.2023.3294248. Epub 2023 Nov 30.
Computed tomography (CT) images are the most commonly used radiographic imaging modality for detecting and diagnosing lumbar diseases. Despite many outstanding advances, computer-aided diagnosis (CAD) of lumbar disc disease remains challenging due to the complexity of pathological abnormalities and poor discrimination between different lesions. Therefore, we propose a Collaborative Multi-Metadata Fusion classification network (CMMF-Net) to address these challenges. The network consists of a feature selection model and a classification model. We propose a novel Multi-scale Feature Fusion (MFF) module that can improve the edge learning ability of the network region of interest (ROI) by fusing features of different scales and dimensions. We also propose a new loss function to improve the convergence of the network to the internal and external edges of the intervertebral disc. Subsequently, we use the ROI bounding box from the feature selection model to crop the original image and calculate the distance features matrix. We then concatenate the cropped CT images, multiscale fusion features, and distance feature matrices and input them into the classification network. Next, the model outputs the classification results and the class activation map (CAM). Finally, the CAM of the original image size is returned to the feature selection network during the upsampling process to achieve collaborative model training. Extensive experiments demonstrate the effectiveness of our method. The model achieved 91.32% accuracy in the lumbar spine disease classification task. In the labelled lumbar disc segmentation task, the Dice coefficient reaches 94.39%. The classification accuracy in the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) reaches 91.82%.
计算机断层扫描（CT）图像是检测和诊断腰椎疾病最常用的放射影像学方法。尽管取得了许多杰出的进展，但由于病理异常的复杂性和不同病变之间的鉴别能力差，腰椎间盘疾病的计算机辅助诊断（CAD）仍然具有挑战性。因此，我们提出了一种协同多源数据融合分类网络（CMMF-Net）来应对这些挑战。该网络由特征选择模型和分类模型组成。我们提出了一种新颖的多尺度特征融合（MFF）模块，可以通过融合不同尺度和维度的特征来提高网络感兴趣区域（ROI）的边缘学习能力。我们还提出了一种新的损失函数，以提高网络对椎间盘内外边缘的收敛性。随后，我们使用特征选择模型中的 ROI 边界框裁剪原始图像并计算距离特征矩阵。然后，我们将裁剪后的 CT 图像、多尺度融合特征和距离特征矩阵拼接起来，并将其输入到分类网络中。接下来，模型输出分类结果和类激活图（CAM）。最后，在上采样过程中，将原始图像大小的 CAM 返回给特征选择网络，以实现协同模型训练。大量实验证明了我们方法的有效性。该模型在腰椎疾病分类任务中达到了 91.32%的准确率。在有标签的腰椎间盘分割任务中，Dice 系数达到了 94.39%。在肺影像数据库联盟和影像数据库资源倡议（LIDC-IDRI）中的分类准确率达到了 91.82%。
