Phaphuangwittayakul Aniwat, Guo Yi, Ying Fangli, Dawod Ahmad Yahya, Angkurawaranon Salita, Angkurawaranon Chaisiri
Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China.
National Engineering Laboratory for Big Data Distribution and Exchange Technologies, Shanghai, China.
Appl Intell (Dordr). 2022;52(7):7320-7338. doi: 10.1007/s10489-021-02782-9. Epub 2021 Sep 25.
Traumatic Brain Injury (TBI) could lead to intracranial hemorrhage (ICH), which has now been identified as a major cause of death after trauma if it is not adequately diagnosed and properly treated within the first 24 hours. CT examination is widely preferred for urgent ICH diagnosis, which enables the fast identification and detection of ICH regions. However, the use of it requires the clinical interpretation by experts to identify the subtypes of ICH. Besides, it is unable to provide the details needed to conduct quantitative assessment, such as the volume and thickness of hemorrhagic lesions, which may have prognostic importance to the decision-making on emergency treatment. In this paper, an optimal deep learning framework is proposed to assist the quantitative assessment for ICH diagnosis and the accurate detection of different subtypes of ICH through head CT scan. Firstly, the format of raw input data is converted from 3D DICOM to NIfTI. Secondly, a pre-trained multi-class semantic segmentation model is applied to each slice of CT images, so as to obtain a precise 3D mask of the whole ICH region. Thirdly, a fine-tuned classification neural network is employed to extract the key features from the raw input data and identify the subtypes of ICH. Finally, a quantitative assessment algorithm is adopted to automatically measure both thickness and volume via the 3D shape mask combined with the output probabilities of the classification network. The results of our extensive experiments demonstrate the effectiveness of the proposed framework where the average accuracy of 96.21 percent is achieved for three types of hemorrhage. The capability of our optimal classification model to distinguish between different types of lesion plays a significant role in reducing the false-positive rate in the existing work. Furthermore, the results suggest that our automatic quantitative assessment algorithm is effective in providing clinically relevant quantification in terms of volume and thickness. It is more important than the qualitative assessment conducted through visual inspection to the decision-making on emergency surgical treatment.
创伤性脑损伤（TBI）可导致颅内出血（ICH），如果在最初24小时内未得到充分诊断和妥善治疗，颅内出血现已被确认为创伤后死亡的主要原因。CT检查因能快速识别和检测ICH区域，被广泛用于紧急ICH诊断。然而，其使用需要专家进行临床解读以识别ICH亚型。此外，它无法提供进行定量评估所需的细节，如出血性病变的体积和厚度，而这些细节对于急诊治疗决策可能具有预后重要性。本文提出了一种优化的深度学习框架，以通过头部CT扫描辅助ICH诊断的定量评估及准确检测不同亚型的ICH。首先，将原始输入数据的格式从3D DICOM转换为NIfTI。其次，将预训练的多类语义分割模型应用于CT图像的每一层，从而获得整个ICH区域的精确3D掩码。第三，采用微调的分类神经网络从原始输入数据中提取关键特征并识别ICH亚型。最后，采用定量评估算法通过3D形状掩码结合分类网络的输出概率自动测量厚度和体积。我们大量实验的结果证明了所提出框架的有效性，对于三种类型的出血，平均准确率达到了96.21%。我们优化的分类模型区分不同类型病变的能力在降低现有工作中的假阳性率方面发挥了重要作用。此外，结果表明我们的自动定量评估算法在提供与临床相关的体积和厚度量化方面是有效的。这对于急诊手术治疗决策比通过目视检查进行的定性评估更为重要。
