First pass gadolinium-enhanced cardiovascular magnetic resonance (CMR) perfusion imaging allows fully quantitative pixel-wise myocardial blood flow (MBF) assessment, with proven diagnostic value for coronary artery disease. Segmental analysis requires manual segmentation of the myocardium. This work presents a fully automatic method of segmenting the left ventricular myocardium from MBF pixel maps, validated on a retrospective dataset of 247 clinical CMR perfusion studies, each including rest and stress images of three slice locations, performed on a 1.5T scanner. Pixel-wise MBF maps were segmented using an automated pipeline including region growing, edge detection, principal component analysis, and active contours to segment the myocardium, detect key landmarks, and divide the myocardium into sectors appropriate for analysis. Automated segmentation results were compared against a manually defined reference standard using three quantitative metrics: Dice coefficient, Cohen Kappa and myocardial border distance. Sector-wise average MBF and myocardial perfusion reserve (MPR) were compared using Pearson's correlation coefficient and Bland-Altman Plots. The proposed method segmented stress and rest MBF maps of 243 studies automatically. Automated and manual myocardial segmentation had an average (± standard deviation) Dice coefficient of 0.86 ± 0.06, Cohen Kappa of 0.86 ± 0.06, and Euclidian distances of 1.47 ± 0.73 mm and 1.02 ± 0.51 mm for the epicardial and endocardial border, respectively. Automated and manual sector-wise MBF and MPR values correlated with Pearson's coefficient of 0.97 and 0.92, respectively, while Bland-Altman analysis showed bias of 0.01 and 0.07 ml/g/min. The validated method has been integrated with our fully automated MBF pixel mapping pipeline to aid quantitative assessment of myocardial perfusion CMR.

Intrusion detection systems (IDSs) play a pivotal role in safeguarding networks and systems against malicious activities. However, the challenge of imbalanced datasets significantly impacts IDS research, skewing learning models towards the majority class and diminishing accuracy for the minority class. This study introduces the Reinforcement Learning (RL) Framework with Oversampling and Undersampling Algorithm (RLFOUA) to address imbalanced datasets. RLFOUA combines RL with diverse resampling algorithms, creating an adaptive learning environment. It integrates the novel True False Rate Synthetic Minority Oversampling Technique (TFRSMOTE) algorithm, emphasizing data-level approaches. Additionally, RLFOUA employs a cost-sensitive approach based on classification metrics. Using the CSE-CIC-IDS2018 and NSL-KDD datasets, RLFOUA demonstrates substantial improvement over existing resampling techniques. Achieving an accuracy of 0.9981 for NSL-KDD and 0.9846 for CSE-CIC-IDS2018, the framework’s performance is evaluated using F1 score, accuracy, precision, recall, and a proposed Index Metric (IM). RLFOUA presents a significant advancement in addressing class imbalance challenges in IDS. It shows an average accuracy improvement of 21.5% compared to the recent resampling technique AESMOTE on the NSL-KDD dataset.

Visualization of coronary computed tomography angiography (CCTA) in standard cardiac planes requires reorientation of the three-dimensional image volume because the heart is double-oblique relative to the axial scan plane. To standardize this process across multi-phase cine CCTA, we developed an automated framework that segments cardiac structures and detects anatomic landmarks to define a patient-specific cardiac coordinate system. Landmark-derived left ventricular (LV) long-axis and LV-to-right ventricular transverse vectors were used to compute a rotation matrix for reorienting all 20 cine CCTA volumes. Six fixed slice planes were then defined to extract three long-axis (LAX) and three short-axis (SAX) slices at each phase. Matched CCTA and cardiac magnetic resonance (MR) slices were compared in 25 patients at diastasis, end-diastole (ED), and end-systole (ES), using automated segmentation-derived measurements of the combined LV cavity and LV myocardium area (LV + LVM). Agreement was evaluated with intraclass correlation coefficients (ICCs), Bland–Altman analysis, and repeated-measures mixed-effects models. ICCs were 0.927 (95% CI, 0.895 to 0.950) at diastasis, 0.950 (95% CI, 0.928 to 0.966) at ED, and 0.919 (95% CI, 0.887 to 0.943) at ES. Bland–Altman analysis showed small CT-positive biases of 1.05, 0.47, and 1.86 cm² at diastasis, ED, and ES, corresponding to relative biases of 2.8%, 1.2%, and 5.8% of the phase-specific mean MR LV + LVM area. Mixed-effects models showed no statistically significant phase-level bias at diastasis or ED, whereas ES showed a small but significant CT-positive bias. These findings support automated CCTA reorientation as an interpretable framework for reproducible LAX/SAX slice extraction and future quantitative functional cine CCTA analysis.