Attention-based hybrid CNN-LSTM and spectral data augmentation for COVID-19 diagnosis from cough sound.

COVID-19 pandemic has fueled the interest in artificial intelligence tools for quick diagnosis to limit virus spreading. Over 60% of people who are infected complain of a dry cough. Cough and other respiratory sounds were used to build diagnosis models in much recent research. We propose in this work, an augmentation pipeline which is applied on the pre-filtered data and uses i) pitch-shifting technique to augment the raw signal and, ii) spectral data augmentation technique SpecAugment to augment the computed mel-spectrograms. A deep learning based architecture that hybridizes convolution neural networks and long-short term memory with an attention mechanism is proposed for building the classification model. The feasibility of the proposed is demonstrated through a set of testing scenarios using the large-scale COUGHVID cough dataset and through a comparison with three baselines models. We have shown that our classification model achieved 91.13% of testing accuracy, 90.93% of sensitivity and an area under the curve of receiver operating characteristic of 91.13%.

Multiatlas Fusion with a Hybrid CT Number Correction Technique for Subject-Specific Pseudo-CT Estimation in the Context of MRI-Only Radiation Therapy.

OBJECTIVE: To propose a hybrid multiatlas fusion and correction approach to estimate a pseudo-computed tomography (pCT) image from T2-weighted brain magnetic resonance (MR) images in the context of MRI-only radiotherapy. MATERIALS AND METHODS: A set of eleven pairs of T2-weighted MR and CT brain images was included. Using leave-one-out cross-validation, atlas MR images were registered to the target MRI with multimetric, multiresolution deformable registration. The subsequent deformations were applied to the atlas CT images, producing uncorrected pCT images. Afterward, a three-dimensional hybrid CT number correction technique was used. This technique uses information about MR intensity, spatial location, and tissue label from segmented MR images with the fuzzy c-means algorithm and combines them in a weighted fashion to correct Hounsfield unit values of the uncorrected pCT images. The corrected pCT images were then fused into a final pCT image. RESULTS: The proposed hybrid approach proved to be performant in correcting Hounsfield unit values in terms of qualitative and quantitative measures. Average correlation was 0.92 and 0.91 for the proposed approach by taking the mean and the median, respectively, compared with 0.86 for the uncorrected unfused version. Average values of dice similarity coefficient for bone were 0.68 and 0.72 for the fused corrected pCT images by taking the mean and the median, respectively, compared with 0.65 for the uncorrected unfused version indicating a significant bone estimation improvement. CONCLUSION: A hybrid fusion and correction method is presented to estimate a pCT image from T2-weighted brain MR images.

A review on methods to estimate a CT from MRI data in the context of MRI-alone RT

Background: In recent years, Radiation Therapy (RT) has undergone many developments and provided progress in the field of cancer treatment. However, dose optimisation each treatment session puts the patient at risk of successive X-Ray exposure from Computed Tomography CT scans since this imaging modality is the reference for dose planning. Add to this difficulties related to contour propagation. Thus, approaches are focusing on the use of MRI as the only modality in RT. In this paper, we review methods for creating pseudo-CT images from MRI data for MRI-alone RT. Each class of methods is explained and underlying works are presented in detail with performance results. We discuss the advantages and limitations of each class. Methods: We classified recent works in deriving a pseudo-CT from MR images into four classes: segmentation-based, intensity-based, atlas-based and hybrid methods and the classification was based on considering the general technique applied. Results: Most research focused on the brain and the pelvic regions. The mean absolute error ranged from 80 to 137 HU and from 36.4 to 74 HU for the brain and pelvis, respectively. In addition, an interest in the Dixon MR sequence is increasing since it has the advantage of producing multiple contrast images with a single acquisition. Conclusion: Radiation therapy is emerging towards the generalisation of MRI-only RT thanks to the advances in techniques for generation of pseudo-CT images and the development of specialised MR sequences favouring bone visualisation. However, a benchmark needs to be established to set in common performance metrics to assess the quality of the generated pseudo-CT and judge on the efficiency of a certain method