Khan Muntakim Mahmud, Chowdhury Muhammad E H, Arefin A S M Shamsul, Podder Kanchon Kanti, Hossain Md Sakib Abrar, Alqahtani Abdulrahman, Murugappan M, Khandakar Amith, Mushtak Adam, Nahiduzzaman Md
Department of Biomedical Physics and Technology, University of Dhaka, Dhaka 1000, Bangladesh.
Department of Electrical Engineering, Qatar University, Doha 2713, Qatar.
Diagnostics (Basel). 2023 Jul 31;13(15):2537. doi: 10.3390/diagnostics13152537.
Intracranial hemorrhage (ICH) occurs when blood leaks inside the skull as a result of trauma to the skull or due to medical conditions. ICH usually requires immediate medical and surgical attention because the disease has a high mortality rate, long-term disability potential, and other potentially life-threatening complications. There are a wide range of severity levels, sizes, and morphologies of ICHs, making accurate identification challenging. Hemorrhages that are small are more likely to be missed, particularly in healthcare systems that experience high turnover when it comes to computed tomography (CT) investigations. Although many neuroimaging modalities have been developed, CT remains the standard for diagnosing trauma and hemorrhage (including non-traumatic ones). A CT scan-based diagnosis can provide time-critical, urgent ICH surgery that could save lives because CT scan-based diagnoses can be obtained rapidly. The purpose of this study is to develop a machine-learning algorithm that can detect intracranial hemorrhage based on plain CT images taken from 75 patients. CT images were preprocessed using brain windowing, skull-stripping, and image inversion techniques. Hemorrhage segmentation was performed using multiple pre-trained models on preprocessed CT images. A U-Net model with DenseNet201 pre-trained encoder outperformed other U-Net, U-Net++, and FPN (Feature Pyramid Network) models with the highest Dice similarity coefficient (DSC) and intersection over union (IoU) scores, which were previously used in many other medical applications. We presented a three-dimensional brain model highlighting hemorrhages from ground truth and predicted masks. The volume of hemorrhage was measured volumetrically to determine the size of the hematoma. This study is essential in examining ICH for diagnostic purposes in clinical practice by comparing the predicted 3D model with the ground truth.
颅内出血（ICH）是指由于颅骨创伤或某些疾病状况导致血液在颅骨内泄漏。ICH通常需要立即进行医疗和手术治疗，因为该疾病死亡率高，有导致长期残疾的可能性，还会引发其他潜在的危及生命的并发症。ICH的严重程度、大小和形态各异，这使得准确识别具有挑战性。较小的出血更容易被漏诊，尤其是在计算机断层扫描（CT）检查周转快的医疗系统中。尽管已经开发了多种神经成像方法，但CT仍然是诊断创伤和出血（包括非创伤性出血）的标准方法。基于CT扫描的诊断能够提供时间紧迫的紧急ICH手术，从而挽救生命，因为基于CT扫描的诊断可以快速获得。本研究的目的是开发一种机器学习算法，该算法能够根据75名患者的平扫CT图像检测颅内出血。CT图像使用脑窗技术、去颅骨技术和图像反转技术进行预处理。在预处理后的CT图像上使用多个预训练模型进行出血分割。具有DenseNet201预训练编码器的U-Net模型在Dice相似系数（DSC）和交并比（IoU）得分方面优于其他U-Net、U-Net++以及特征金字塔网络（FPN）模型，这些模型此前已在许多其他医学应用中使用。我们展示了一个三维脑模型，突出显示了来自真实情况和预测掩码的出血情况。通过体积测量来确定血肿的大小。通过将预测的三维模型与真实情况进行比较，本研究对于在临床实践中检查ICH以用于诊断目的至关重要。
