Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN

Since the appearance of the COVID-19 pandemic (at the end of 2019, Wuhan, China), the recognition of COVID-19 with medical imaging has become an active research topic for the machine learning and computer vision community. This paper is based on the results obtained from the 2021 COVID-19 SPGC challenge, which aims to classify volumetric CT scans into normal, COVID-19, or community-acquired pneumonia (Cap) classes. To this end, we proposed a deep-learning-based approach (CNR-IEMN) that consists of two main stages. In the first stage, we trained four deep learning architectures with a multi-tasks strategy for slice-level classification. In the second stage, we used the previously trained models with an XG-boost classifier to classify the whole CT scan into normal, COVID-19, or Cap classes. Our approach achieved a good result on the validation set, with an overall accuracy of 87.75% and 96.36%, 52.63%, and 95.83% sensitivities for COVID-19, Cap, and normal, respectively. On the other hand, our approach achieved fifth place on the three test datasets of SPGC in the COVID-19 challenge, where our approach achieved the best result for COVID-19 sensitivity. In addition, our approach achieved second place on two of the three testing sets.

Deep Learning Method for Quantitative Analysis of Covid -19 using CT Chest Images

The manual diagnostic tests performed in laboratories for pandemic disease such as COVID19 is time-consuming, requires skills and expertise of the performer to yield accurate results. Moreover, it is very cost ineffective as the cost of test kits is high and also requires well-equipped labs to conduct them. Thus, other means of diagnosing the patients with presence of SARS-COV2 (the virus responsible for COVID19) must be explored. A radiography method like chest CT images is one such means that can be utilized for diagnosis of COVID19. The radio-graphical changes observed in CT images of COVID19 patient helps in developing a deep learning-based method for extraction of graphical features which are then used for automated diagnosis of the disease ahead of laboratory-based testing. The proposed work suggests an Artificial Intelligence (AI) based technique for rapid diagnosis of COVID19 from given volumetric CT images of patient’s chest by extracting its visual features and then using these features in the deep learning module. The proposed convolutional neural network is deployed for classifying the infectious and non-infectious SARS-COV2 subjects. The proposed network utilizes 746 chests scanned CT images of which 349 images belong to COVID19 positive cases while remaining 397 belong negative cases of COVID19. The extensive experiment has been completed with the accuracy of 98.4 %, sensitivity of 98.5 %, the specificity of 98.3 %, the precision of 97.1 %, F1score of 97.8 %. The obtained result shows the outstanding performance for classification of infectious and non-infectious for COVID19 cases.

COVID-19 clinico-radiological mismatch: a proposal for a novel combined morphologic/volumetric CT severity score with blinded validation

Background Some COVID-19 patients with similar quantitative CT measurements had variable clinical presentation and outcome. The absence of reasonable clinical explanations, such as pre-existing comorbidities or vascular complications, adds to the confusion. The authors believed that neglecting the impact of certain severe morphologic features could be an alternative radiological explanation. This study aims to optimize the initial CT staging of COVID-19 and propose a new combined morphologic/volumetric CT severity index (CTSI) to solve this clinico-radiological mismatch. Results This multi-center study included two major steps. The first step of the study entailed a standardized combined morphologic/volumetric CT severity analyses to propose a new optimized CTSI. This was conducted retrospectively during the period from June till September 2020. It included 379 acutely symptomatic COVID-19 patients. They were clinically classified according to their oxygen saturation and respiratory therapeutic requirements into three groups: group A (mild 298/79%), group B (borderline severity 57/15%), and group C (severe/critical 24/6%). The morphologic and volumetric assessment of their HRCT was analyzed according to severity, by two consultant radiologists in consensus. A new 25 point-CTSI has been created, combining eight morphological CT patterns [M1:M8; 8 points] and four grades of volumetric scores [S1:S4; 17 points]. The addition of the M5 pattern (air bubble sign), M6 pattern (early fibrosis and architectural distortion), or M7 pattern (crazy-paving) proved to increase the clinical severity. The second step of the study entailed a standardized blinded/independent validation analysis for the proposed CTSI. This was prospectively conducted on other 132 patients during October 2020 and independently performed by other two consultant radiologists. Validation results reached 80.2% sensitivity, 91.8% specificity, AUROC-curve = 0.8356, and 90.9% accuracy. Conclusion A new optimized CTSI with accepted validation is proposed for initial staging of COVID-19 patients, using combined morphologic/volumetric assessment instead of the quantitative assessment alone. It could solve the clinico-radiological mismatch among patients with similar quantitative CT results and variable clinical presentation during the absence of pre-existing comorbidities or vascular complications.

Intracerebral Hemorrhage with Intraventricular Extension Associated with Loss of Consciousness at Symptom Onset

Background In patients with spontaneous intracerebral hemorrhage (ICH), pre-hospital markers of disease severity might be useful to potentially triage patients to undergo early interventions. Objective Here, we tested whether loss of consciousness (LOC) at the onset of ICH is associated with intraventricular hemorrhage (IVH) on brain computed tomography (CT). Methods Among 3000 ICH cases from ERICH (Ethnic/Racial Variations of Intracerebral Hemorrhage study, NS069763), we included patients with complete ICH/IVH volumetric CT measurements and excluded those with seizures at ICH onset. Trained investigators extracted data from medical charts. Mental status at symptom onset (categorized as alert/oriented, alert/confused, drowsy/somnolent, coma/unresponsive/posturing) and 3-month disability (modified Rankin score, mRS) were assessed through standardized interviews of participants or dedicated proxies. We used logistic regression and mediation analysis to assess relationships between LOC, IVH, and unfavorable outcome (mRS 4–6). Results Two thousand seven hundred and twenty-four patients met inclusion criteria. Median admission Glasgow Coma Score was 15 (interquartile range 11–15). 46% had IVH on admission or follow-up CT. Patients with LOC (mental status: coma/unresponsive, n = 352) compared to those without LOC (all other mental status, n = 2372) were younger (60 vs. 62 years, p = 0.005) and had greater IVH frequency (77 vs. 41%, p < 0.001), greater peak ICH volumes (28 vs. 11 ml, p < 0.001), greater admission systolic blood pressure (200 vs. 184 mmHg, p < 0.001), and greater admission serum glucose (158 vs. 127 mg/dl, p < 0.001). LOC was independently associated with IVH presence (odds ratio, OR, 2.6, CI 1.9–3.5) and with unfavorable outcome (OR 3.05, CI 1.96–4.75). The association between LOC and outcome was significantly mediated by IVH (beta = 0.24, bootstrapped CI 0.17–0.32). Conclusion LOC at ICH onset may be a useful pre-hospital marker to identify patients at risk of having or developing IVH.

Cascaded Dilated Deep Residual Network for Volumetric Liver Segmentation From CT Image

Volumetric liver segmentation is a prerequisite for liver transplantation and radiation therapy planning. In this paper, dilated deep residual network (DDRN) has been proposed for automatic segmentation of liver from CT images. The combination of three parallel DDRN is cascaded with fourth DDRN in order to get final result. The volumetric CT data of 40 subjects belongs to “Combined Healthy Abdominal Organ Segmentation” (CHAOS) challenge 2019 is utilized to evaluate the proposed method. Input image converted into three images using windowing ranges and fed to three DDRN. The output of three DDRN along with original image fed to the fourth DDRN as an input. The output of cascaded network is compared with the three parallel DDRN individually. Obtained results were quantitatively evaluated with various evaluation parameters. The results were submitted to online evaluation system, and achieved average dice coefficient is 0.93±0.02; average symmetric surface distance (ASSD) is 4.89±0.91. In conclusion, obtained results are prominent and consistent.