Liver disease classification from ultrasound using multi-scale CNN.

PURPOSE: Ultrasound (US) is the preferred modality for fatty liver disease diagnosis due to its noninvasive, real-time, and cost-effective imaging capabilities. However, traditional B-mode US is qualitative, and therefore, the assessment is very subjective. Computer-aided diagnostic tools can improve the specificity and sensitivity of US and help clinicians to perform uniform diagnoses. METHODS: In this work, we propose a novel deep learning model for nonalcoholic fatty liver disease classification from US data. We design a multi-feature guided multi-scale residual convolutional neural network (CNN) architecture to capture features of different receptive fields. B-mode US images are combined with their corresponding local phase filtered images and radial symmetry transformed images as multi-feature inputs for the network. Various fusion strategies are studied to improve prediction accuracy. We evaluate the designed network architectures on B-mode in vivo liver US images collected from 55 subjects. We also provide quantitative results by comparing our proposed multi-feature CNN architecture against traditional CNN designs and machine learning methods. RESULTS: Quantitative results show an average classification accuracy above 90% over tenfold cross-validation. Our proposed method achieves a 97.8% area under the ROC curve (AUC) for the patient-specific leave-one-out cross-validation (LOOCV) evaluation. Comprehensive validation results further demonstrate that our proposed approaches achieve significant improvements compared to training mono-feature CNN architectures ([Formula: see text]). CONCLUSIONS: Feature combination is valuable for the traditional classification methods, and the use of multi-scale CNN can improve liver classification accuracy. Based on the promising performance, the proposed method has the potential in practical applications to help radiologists diagnose nonalcoholic fatty liver disease.

Chest X-ray image phase features for improved diagnosis of COVID-19 using convolutional neural network.

PURPOSE: Recently, the outbreak of the novel coronavirus disease 2019 (COVID-19) pandemic has seriously endangered human health and life. In fighting against COVID-19, effective diagnosis of infected patient is critical for preventing the spread of diseases. Due to limited availability of test kits, the need for auxiliary diagnostic approach has increased. Recent research has shown radiography of COVID-19 patient, such as CT and X-ray, contains salient information about the COVID-19 virus and could be used as an alternative diagnosis method. Chest X-ray (CXR) due to its faster imaging time, wide availability, low cost, and portability gains much attention and becomes very promising. In order to reduce intra- and inter-observer variability, during radiological assessment, computer-aided diagnostic tools have been used in order to supplement medical decision making and subsequent management. Computational methods with high accuracy and robustness are required for rapid triaging of patients and aiding radiologist in the interpretation of the collected data. METHOD: In this study, we design a novel multi-feature convolutional neural network (CNN) architecture for multi-class improved classification of COVID-19 from CXR images. CXR images are enhanced using a local phase-based image enhancement method. The enhanced images, together with the original CXR data, are used as an input to our proposed CNN architecture. Using ablation studies, we show the effectiveness of the enhanced images in improving the diagnostic accuracy. We provide quantitative evaluation on two datasets and qualitative results for visual inspection. Quantitative evaluation is performed on data consisting of 8851 normal (healthy), 6045 pneumonia, and 3323 COVID-19 CXR scans. RESULTS: In Dataset-1, our model achieves 95.57% average accuracy for a three classes classification, 99% precision, recall, and F1-scores for COVID-19 cases. For Dataset-2, we have obtained 94.44% average accuracy, and 95% precision, recall, and F1-scores for detection of COVID-19. CONCLUSIONS: Our proposed multi-feature-guided CNN achieves improved results compared to single-feature CNN proving the importance of the local phase-based CXR image enhancement. Future work will involve further evaluation of the proposed method on a larger-size COVID-19 dataset as they become available.

Steroid-Mediated Decrease in Blood Mesenchymal Stem Cells in Liver Transplant could Impact Long-Term Recovery.

Orthotopic liver transplant (OLT) remains the standard of care for end stage liver disease. To circumvent allo-rejection, OLT subjects receive gluococorticoids (GC). We investigated the effects of GC on endogenous mesenchymal stem (stromal) cells (MSCs) in OLT. This question is relevant because MSCs have regenerative potential and immune suppressor function. Phenotypic analyses of blood samples from 12 OLT recipients, at pre-anhepatic, anhepatic and post-transplant (2 h, Days 1 and 5) indicated a significant decrease in MSCs after GC injection. The MSCs showed better recovery in the blood from subjects who started with relatively low MSCs as compared to those with high levels at the prehepatic phase. This drop in MSCs appeared to be linked to GC since similar change was not observed in liver resection subjects. In order to understand the effects of GC on decrease MSC migration, in vitro studies were performed in transwell cultures. Untreated MSCs could not migrate towards the GC-exposed liver tissue, despite CXCR4 expression and the production of inflammatory cytokines from the liver cells. GC-treated MSCs were inefficient with respect to migration towards CXCL12, and this correlated with retracted cytoskeleton and motility. These dysfunctions were partly explained by decreases in the CXCL12/receptor axis. GC-associated decrease in MSCs in OLT recipients recovered post-transplant, despite poor migratory ability towards GC-exposed liver. In total, the study indicated that GC usage in transplant needs to be examined to determine if this could be reduced or avoided with adjuvant cell therapy.