To precisely determine the severity of COVID-19-related pneumonia, computed tomography (CT) is an imaging modality beneficial for patient monitoring and therapy planning. Thus, we aimed to develop a deep learning-based image segmentation model to automatically assess lung lesions related to COVID-19 infection and calculate the total severity score (TSS). The entire dataset consisted of 124 COVID-19 patients acquired from Chulabhorn Hospital, divided into 28 cases without lung lesions and 96 cases with lung lesions categorized severity by radiologists regarding TSS. The model used a 3D-UNet along with DenseNet and ResNet models that had already been trained to separate the lobes of the lungs and figure out the percentage of lung involvement due to COVID-19 infection. It also used the Dice similarity coefficient (DSC) to measure TSS. Our final model, consisting of 3D-UNet integrated with DenseNet169, achieved segmentation of lung lobes and lesions with the Dice similarity coefficients of 91.52% and 76.89%, respectively. The calculated TSS values were similar to those evaluated by radiologists, with an R2 of 0.842. The correlation between the ground-truth TSS and model prediction was greater than that of the radiologist, which was 0.890 and 0.709, respectively.

Evaluation of Radiation Dose in Computed Tomography Angiography before Transcatheter Aortic Valve Implantation

Traumatic pseudoaneurysms of the vertebrobasilar artery are rare and may occur without skull fractures. Standard CTA may miss or misinterpret these lesions due to technical limitations and low clinical suspicion, leading to significant diagnostic and medicolegal errors; We present two cases of fatal isolated traumatic subarachnoid hemorrhage (iTSAH), where optimized thin-slice CTA, identified pseudoaneurysms of the PICA and VA (V4 segment), confirmed by histopathology. Graphical abstract