Ouyang Zihui, Zhang Peng, Pan Weifan, Li Qiang
Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.
MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.
Med Phys. 2022 May;49(5):3067-3079. doi: 10.1002/mp.15536. Epub 2022 Feb 21.
The automatic recognition of human body parts in three-dimensional medical images is important in many clinical applications. However, methods presented in prior studies have mainly classified each two-dimensional (2D) slice independently rather than recognizing a batch of consecutive slices as a specific body part.
In this study, we aim to develop a deep learning-based method designed to automatically divide computed tomography (CT) and magnetic resonance imaging (MRI) scans into five consecutive body parts: head, neck, chest, abdomen, and pelvis.
A deep learning framework was developed to recognize body parts in two stages. In the first preclassification stage, a convolutional neural network (CNN) using the GoogLeNet Inception v3 architecture and a long short-term memory (LSTM) network were combined to classify each 2D slice; the CNN extracted information from a single slice, whereas the LSTM employed rich contextual information among consecutive slices. In the second postprocessing stage, the input scan was further partitioned into consecutive body parts by identifying the optimal boundaries between them based on the slice classification results of the first stage. To evaluate the performance of the proposed method, 662 CT and 1434 MRI scans were used.
Our method achieved a very good performance in 2D slice classification compared with state-of-the-art methods, with overall classification accuracies of 97.3% and 98.2% for CT and MRI scans, respectively. Moreover, our method further divided whole scans into consecutive body parts with mean boundary errors of 8.9 and 3.5 mm for CT and MRI data, respectively.
The proposed method significantly improved the slice classification accuracy compared with state-of-the-art methods, and further accurately divided CT and MRI scans into consecutive body parts based on the results of slice classification. The developed method can be employed as an important step in various computer-aided diagnosis and medical image analysis schemes.
在许多临床应用中，三维医学图像中人体部位的自动识别非常重要。然而，先前研究中提出的方法主要是独立地对每个二维（2D）切片进行分类，而不是将一批连续的切片识别为特定的人体部位。
在本研究中，我们旨在开发一种基于深度学习的方法，用于将计算机断层扫描（CT）和磁共振成像（MRI）扫描自动划分为五个连续的身体部位：头部、颈部、胸部、腹部和骨盆。
开发了一个深度学习框架，分两个阶段识别身体部位。在第一个预分类阶段，将使用GoogLeNet Inception v3架构的卷积神经网络（CNN）和长短期记忆（LSTM）网络相结合，对每个2D切片进行分类；CNN从单个切片中提取信息，而LSTM利用连续切片之间丰富的上下文信息。在第二个后处理阶段，根据第一阶段的切片分类结果，通过确定它们之间的最佳边界，将输入扫描进一步划分为连续的身体部位。为了评估所提出方法的性能，使用了662例CT扫描和1434例MRI扫描。
与现有方法相比，我们的方法在2D切片分类中取得了非常好的性能，CT扫描和MRI扫描的总体分类准确率分别为97.3%和98.2%。此外，我们的方法进一步将整个扫描划分为连续的身体部位，CT和MRI数据的平均边界误差分别为8.9毫米和3.5毫米。
与现有方法相比，所提出的方法显著提高了切片分类准确率，并根据切片分类结果进一步将CT和MRI扫描准确划分为连续的身体部位。所开发的方法可作为各种计算机辅助诊断和医学图像分析方案中的重要一步。
