Non-calcified plaque-based coronary stenosis grading in contrast enhanced CT.

BACKGROUND: The high mortality rate associated with coronary heart disease has led to state-of-the-art non-invasive methods for cardiac diagnosis including computed tomography and magnetic resonance imaging. However, stenosis computation and clinical assessment of non-calcified plaques has been very challenging due to their ambiguous intensity response in CT i.e. a significant overlap with surrounding muscle tissues and blood. Accordingly, this research presents an approach for computation of coronary stenosis by investigating cross-sectional lumen behaviour along the length of 3D coronary segments. METHODS: Non-calcified plaques are characterized by comparatively lower-intensity values with respect to the surrounding. Accordingly, segment-wise orthogonal volume was reconstructed in 3D space using the segmented coronary tree. Subsequently, the cross sectional volumetric data was investigated using proposed CNN-based plaque quantification model and subsequent stenosis grading in clinical context was performed. In the last step, plaque-affected orthogonal volume was further investigated by comparing vessel-wall thickness and lumen area obstruction w.r.t. expert-based annotations to validate the stenosis grading performance of model. RESULTS: The experimental data consists of clinical CT images obtained from the Rotterdam CT repository leading to 600 coronary segments and subsequent 15786 cross-sectional images. According to the results, the proposed method quantified coronary vessel stenosis i.e. severity of the non-calcified plaque with an overall accuracy of 83%. Moreover, for individual grading, the proposed model show promising results with accuracy equal to 86%, 90% and 79% respectively for severe, moderate and mild stenosis. The stenosis grading performance of the proposed model was further validated by performing lumen-area versus wall-thickness analysis as per annotations of manual experts. The statistical results for lumen area analysis precisely correlates with the quantification performance of the model with a mean deviation of 5% only. CONCLUSION: The overall results demonstrates capability of the proposed model to grade the vessel stenosis with reasonable accuracy and precision equivalent to human experts.

Multi-step rainfall forecasting using deep learning approach.

Rainfall prediction is immensely crucial in daily life routine as well as for water resource management, stochastic hydrology, rain run-off modeling and flood risk mitigation. Quantitative prediction of rainfall time series is extremely challenging as compared to other meteorological parameters due to its variability in local features that involves temporal and spatial scales. Consequently, this requires a highly complex system having an advance model to accurately capture the highly non linear processes occurring in the climate. The focus of this work is direct prediction of multistep forecasting, where a separate time series model for each forecasting horizon is considered and forecasts are computed using observed data samples. Forecasting in this method is performed by proposing a deep learning approach, i.e, Temporal Deep Belief Network (DBN). The best model is selected from several baseline models on the basis of performance analysis metrics. The results suggest that the temporal DBN model outperforms the conventional Convolutional Neural Network (CNN) specifically on rainfall time series forecasting. According to our experimentation, a modified DBN with hidden layes (300-200-100-10) performs best with 4.59E-05, 0.0068 and 0.94 values of MSE, RMSE and R value respectively on testing samples. However, we found that training DBN is more exhaustive and computationally intensive than other deep learning architectures. Findings of this research can be further utilized as basis for the advance forecasting of other weather parameters with same climate conditions.