MR Imaging in Neurocritical Care

Advancing glioma diagnosis: Integrating custom U-Net and VGG-16 for improved grading in MR imaging

In the realm of medical imaging, the precise segmentation and classification of gliomas represent fundamental challenges with profound clinical implications. Leveraging the BraTS 2018 dataset as a standard benchmark, this study delves into the potential of advanced deep learning models for addressing these challenges. We propose a novel approach that integrates a customized U-Net for segmentation and VGG-16 for classification. The U-Net, with its tailored encoder-decoder pathways, accurately identifies glioma regions, thus improving tumor localization. The fine-tuned VGG-16, featuring a customized output layer, precisely differentiates between low-grade and high-grade gliomas. To ensure consistency in data pre-processing, a standardized methodology involving gamma correction, data augmentation, and normalization is introduced. This novel integration surpasses existing methods, offering significantly improved glioma diagnosis, validated by high segmentation dice scores (WT: 0.96, TC: 0.92, ET: 0.89), and a remarkable overall classification accuracy of 97.89%. The experimental findings underscore the potential of integrating deep learning-based methodologies for tumor segmentation and classification in enhancing glioma diagnosis and formulating subsequent treatment strategies.

MRI and the Critical Care Patient: Clinical, Operational, and Financial Challenges

Neuroimaging in conjunction with a neurologic examination has become a valuable resource for today's intensive care unit (ICU) physicians. Imaging provides critical information during the assessment and ongoing neuromonitoring of patients for toxic-metabolic or structural injury of the brain. A patient's condition can change rapidly, and interventions may require imaging. When making this determination, the benefit must be weighed against possible risks associated with intrahospital transport. The patient's condition is assessed to decide if they are stable enough to leave the ICU for an extended period. Intrahospital transport risks include adverse events related to the physical nature of the transport, the change in the environment, or relocating equipment used to monitor the patient. Adverse events can be categorized as minor (e.g., clinical decompensation) or major (e.g., requiring immediate intervention) and may occur in preparation or during transport. Regardless of the type of event experienced, any intervention during transport impacts the patient and may lead to delayed treatment and disruption of critical care. This review summarizes the commentary on the current literature on the associated risks and provides insight into the costs as well as provider experiences. Approximately, one-third of patients who are transported from the ICU to an imaging suite may experience an adverse event. This creates an additional risk for extending a patient's stay in the ICU. The delay in obtaining imaging can negatively impact the patient's treatment plan and affect long-term outcomes as increased disability or mortality. Disruption of ICU therapy can decrease respiratory function after the patient returns from transport. Because of the complex care team needed for patient transport, the staff time alone can cost $200 or more. New technologies and advancements are needed to reduce patient risk and improve safety.

A comparative study for glioma classification using deep convolutional neural networks

Gliomas are a type of central nervous system (CNS) tumor that accounts for the most of malignant brain tumors. The World Health Organization (WHO) divides gliomas into four grades based on the degree of malignancy. Gliomas of grades I-II are considered low-grade gliomas (LGGs), whereas gliomas of grades III-IV are termed high-grade gliomas (HGGs). Accurate classification of HGGs and LGGs prior to malignant transformation plays a crucial role in treatment planning. Magnetic resonance imaging (MRI) is the cornerstone for glioma diagnosis. However, examination of MRI data is a time-consuming process and error prone due to human intervention. In this study we introduced a custom convolutional neural network (CNN) based deep learning model trained from scratch and compared the performance with pretrained AlexNet, GoogLeNet and SqueezeNet through transfer learning for an effective glioma grade prediction. We trained and tested the models based on pathology-proven 104 clinical cases with glioma (50 LGGs, 54 HGGs). A combination of data augmentation techniques was used to expand the training data. Five-fold cross-validation was applied to evaluate the performance of each model. We compared the models in terms of averaged values of sensitivity, specificity, F1 score, accuracy, and area under the receiver operating characteristic curve (AUC). According to the experimental results, our custom-design deep CNN model achieved comparable or even better performance than the pretrained models. Sensitivity, specificity, F1 score, accuracy and AUC values of the custom model were 0.980, 0.963, 0.970, 0.971 and 0.989, respectively. GoogLeNet showed better performance than AlexNet and SqueezeNet in terms of accuracy and AUC with a sensitivity, specificity, F1 score, accuracy, and AUC values of 0.980, 0.889, 0.933, 0.933, and 0.987, respectively. AlexNet yielded a sensitivity, specificity, F1 score, accuracy, and AUC values of 0.940, 0.907, 0.922, 0.923 and 0.970, respectively. As for SqueezeNet, the sensitivity, specificity, F1 score, accuracy, and AUC values were 0.920, 0.870, 0.893, 0.894, and 0.975, respectively. The results have shown the effectiveness and robustness of the proposed custom model in classifying gliomas into LGG and HGG. The findings suggest that the deep CNNs and transfer learning approaches can be very useful to solve classification problems in the medical domain.