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Author(s):  
Abdelmoughit Hosni ◽  
Siham EL HADDAD ◽  
Nazik ALLALI ◽  
Latifa CHAT

To highlight the risk of septic complications following a pulmonary embolism (PE), we report the case of an elderly patient, that was hospitalized for a PE, an had progressively worsen his respiratory and septic state. Chest CT slices showed a gangrenous lung segment with an associated pleuropulmonary abscess.


2021 ◽  
Author(s):  
Somoballi Ghoshal ◽  
Partha Bhowmick ◽  
Amlan Chakrabarti ◽  
Susmita Sur-Kolay ◽  
Sanjukta Chakravorti ◽  
...  

Author(s):  
Bingzhi Chen ◽  
Yishu Liu ◽  
Zheng Zhang ◽  
Yingjian Li ◽  
Zhao Zhang ◽  
...  

Many studies on automated COVID-19 diagnosis have advanced rapidly with the increasing availability of large-scale CT annotated datasets. Inevitably, there are still a large number of unlabeled CT slices in the existing data sources since it requires considerable consuming labor efforts. Notably, cinical experience indicates that the neighboring CT slices may present similar symptoms and signs. Inspired by such wisdom, we propose DACE, a novel CNN-based deep active context estimation framework, which leverages the unlabeled neighbors to progressively learn more robust feature representations and generate a well-performed classifier for COVID-19 diagnosis. Specifically, the backbone of the proposed DACE framework is constructed by a well-designed Long-Short Hierarchical Attention Network (LSHAN), which effectively incorporates two complementary attention mechanisms, i.e., short-range channel interactions (SCI) module and long-range spatial dependencies (LSD) module, to learn the most discriminative features from CT slices. To make full use of such available data, we design an efficient context estimation criterion to carefully assign the additional labels to these neighbors. Benefiting from two complementary types of informative annotations from -nearest neighbors, i.e., the majority of high-confidence samples with pseudo labels and the minority of low-confidence samples with hand-annotated labels, the proposed LSHAN can be fine-tuned and optimized in an incremental learning manner. Extensive experiments on the Clean-CC-CCII dataset demonstrate the superior performance of our method compared with the state-of-the-art baselines.


2021 ◽  
Author(s):  
En Zhou Ye ◽  
En Hui Ye ◽  
Run Zhou Ye

Introduction: Analysis of multimodal medical images often requires the selection of one or many anatomical regions of interest (ROIs) for extraction of useful statistics. This task can prove laborious when a manual approach is used. We have previously developed a user-friendly software tool for image-to-image translation using deep learning. Therefore, we present herein an update to the DeepImageTranslator software with the addiction of a tool for multimodal medical image segmentation analysis (hereby referred to as the MMMISA). Methods: The MMMISA was implemented using the Tkinter library. Backend computations were implemented using the Pydicom, Numpy, and OpenCV libraries. We tested our software using 4188 whole-body axial 2-deoxy-2-[18F]-fluoroglucose-position emission tomography/computed tomography ([18F]-FDG-PET/CT) slices of 10 patients from the ACRIN-HNSCC (American College of Radiology Imaging Network-Head and Neck Squamous Cell Carcinoma) database. Using the deep learning software DeepImageTranslator, a model was trained with 36 randomly selected CT slices and manually labelled semantic segmentation maps. Utilizing the trained model, all the CT scans of the 10 HNSCC patients were segmented with high accuracy. Segmentation maps generated using the deep convolutional network were then used to measure organ specific [18F]-FDG uptake. We also compared measurements performed using the MMMISA and those made with manually selected ROIs. Results: The MMMISA is a tool that allows user to select ROIs based on deep learning-generated segmentation maps and to compute accurate statistics for these ROIs based on coregistered multimodal images. We found that organ-specific [18F]-FDG uptake measured using multiple manually selected ROIs is concordant with whole-tissue measurements made with segmentation maps using the MMMISA tool.


2021 ◽  
Vol 17 (4) ◽  
pp. 101-118
Author(s):  
Nandhini Abirami ◽  
Durai Raj Vincent ◽  
Seifedine Kadry

Early and automatic segmentation of lung infections from computed tomography images of COVID-19 patients is crucial for timely quarantine and effective treatment. However, automating the segmentation of lung infection from CT slices is challenging due to a lack of contrast between the normal and infected tissues. A CNN and GAN-based framework are presented to classify and then segment the lung infections automatically from COVID-19 lung CT slices. In this work, the authors propose a novel method named P2P-COVID-SEG to automatically classify COVID-19 and normal CT images and then segment COVID-19 lung infections from CT images using GAN. The proposed model outperformed the existing classification models with an accuracy of 98.10%. The segmentation results outperformed existing methods and achieved infection segmentation with accurate boundaries. The Dice coefficient achieved using GAN segmentation is 81.11%. The segmentation results demonstrate that the proposed model outperforms the existing models and achieves state-of-the-art performance.


2021 ◽  
pp. 185-200
Author(s):  
S. Arunmozhi ◽  
Vaddi Satya Sai Sarojini ◽  
T. Pavithra ◽  
Varsha Varghese ◽  
V. Deepti ◽  
...  

2021 ◽  
Author(s):  
Hanguang XIAO ◽  
Zhiqiang RAN ◽  
Shingo MABU ◽  
Banglin ZHANG ◽  
Bolong ZHANG ◽  
...  

Abstract The coronavirus disease 2019 (COVID-19) epidemic has spread worldwide and the healthcare system is in crisis. Accurate, automated and rapid segmentation of COVID-19 lesion in computed tomography (CT) images can help doctors diagnose and provide prognostic information. However, the variety of lesions and small regions of early lesion complicate their segmentation. To solve these problems, we propose a new SAUNet++ model with squeeze excitation residual (SER) module and atrous spatial pyramid pooling (ASPP) module. The SER module can assign more weights to more important channels and mitigate the problem of gradient disappearance, the ASPP module can obtain context information by atrous convolution using various sampling rates. In addition, the generalized dice loss (GDL) can reduce the correlation between lesion size and dice loss, and is introduced to solve the problem of small regions segmentation. We collected multinational CT scan data from China, Italy and Russia and conducted extensive experiments. In the experiments, SAUNet++ and GDL were compared to advanced segmentation models and popular loss functions, respectively. The experimental results demonstrated that our methods can effectively improve the accuracy of COVID-19 lesion segmentation on the dice similarity coefficient (our: 87.38% VS U-Net++: 86.08%), sensitivity (our: 93.28% VS U-Net++: 89.85%) and hausdorff distance (our: 19.99mm VS U-Net++: 27.69mm), respectively.


2021 ◽  
pp. 2100844
Author(s):  
Gael Dournes ◽  
Chase S. Hall ◽  
Matthew M. Willmering ◽  
Alan S. Brody ◽  
Julie Macey ◽  
...  

RationaleChest computed tomography (CT) remains the imaging standard for demonstrating cystic fibrosis airway structural disease in vivo. However, visual scorings as an outcome measure are time-consuming, require training, and lack high reproducibility.ObjectiveTo validate a fully automated artificial intelligence-driven scoring of cystic fibrosis lung disease severity.MethodsData were retrospectively collected in three cystic fibrosis reference centers, between 2008 and 2020, in 184 patients 4 to 54-years-old. An algorithm using three two-dimensional convolutional neural networks was trained with 78 patients’ CTs (23 530 CT slices) for the semantic labeling of bronchiectasis, peribronchial thickening, bronchial mucus, bronchiolar mucus, and collapse/consolidation. 36 patients’ CTs (11 435 CT slices) were used for testing versus ground-truth labels. The method's clinical validity was assessed in an independent group of 70 patients with or without lumacaftor/ivacaftor treatment (n=10 and 60, respectively) with repeat examinations. Similarity and reproducibility were assessed using Dice coefficient, correlations using Spearman test, and paired comparisons using Wilcoxon rank test.Measurement and main resultsThe overall pixelwise similarity of artificial intelligence-driven versus ground-truth labels was good (Dice coefficient=0.71). All artificial intelligence-driven volumetric quantifications had moderate to very good correlations to a visual imaging scoring (p<0.001) and fair to good correlations to FEV1% at pulmonary function test (p<0.001). Significant decreases in peribronchial thickening (p=0.005), bronchial mucus (p=0.005), bronchiolar mucus (p=0.007) volumes were measured in patients with lumacaftor/ivacaftor. Conversely, bronchiectasis (p=0.002) and peribronchial thickening (p=0.008) volumes increased in patients without lumacaftor/ivacaftor. The reproducibility was almost perfect (Dice>0.99).ConclusionArtificial intelligence allows a fully automated volumetric quantification of cystic fibrosis-related modifications over an entire lung. The novel scoring system could provide a robust disease outcome in the era of effective CFTR modulator therapy.


2021 ◽  
Author(s):  
Yunan Wu ◽  
Arne Schmidt ◽  
Enrique Hernandez Sanchez ◽  
Rafael Molina ◽  
Aggelos K. Katsaggelos

Intracranial hemorrhage (ICH) is a life-threatening emergency with high rates of mortality and morbidity. Rapid and accurate detection of ICH is crucial for patients to get a timely treatment. In order to achieve the automatic diagnosis of ICH, most deep learning models rely on huge amounts of slice labels for training. Unfortunately, the manual annotation of CT slices by radiologists is time-consuming and costly. To diagnose ICH, in this work, we propose to use an attention-based multiple instance learning (Att-MIL) approach implemented through the combination of an attention-based convolutional neural network (Att-CNN) and a variational Gaussian process for multiple instance learning (VGPMIL). Only labels at scan-level are necessary for training. Our method (a) trains the model using scan labels and assigns each slice with an attention weight, which can be used to provide slice-level predictions, and (b) uses the VGPMIL model based on low-dimensional features extracted by the Att-CNN to obtain improved predictions both at slice and scan levels. To analyze the performance of the proposed approach, our model has been trained on 1150 scans from an RSNA dataset and evaluated on 490 scans from an external CQ500 dataset. Our method outperforms other methods using the same scan-level training and is able to achieve comparable or even better results than other methods relying on slice-level annotations.


2021 ◽  
Author(s):  
Sotiris Panagi ◽  
Anastasia Hadjiconstanti ◽  
George Charitou ◽  
Demetris Kaolis ◽  
Ioannis Petrou ◽  
...  

Abstract Cranio-caudal respiratory motion and liver activity cause a variety of complex myocardial perfusion (MP) artifacts, especially in the inferior myocardial wall, that may also mask cardiac defects. To assess and characterize such artifacts, an anthropomorphic thorax with moving thoracic phantoms can be utilized in SPECT MP imaging. In this study, a liver phantom was developed, and anatomically added into an anthropomorphic phantom, that encloses an ECG beating cardiac phantom and breathing lungs phantom. A cranio-caudal respiratory motion was also developed for the liver phantom and it was synchronized with the corresponding ones of the cardiac and lungs phantoms. This continuous motion could also be further divided into dynamic respiratory phases, from end-exhalation to end-inspiration, to perform SPECT acquisitions in different respiratory phases. The motion parameters, displacements and volumes, were validated by the acquired CT slices, the OsiriX and Vitrea software. Sample SPECT/16-slice-CT myocardial MP acquisitions were also performed and compared to the literature. The cardiac, lungs and liver phantoms can precisely perform, in time interval of 0.1 sec, physiological thoracic motions within an anthropomorphic thorax. This dynamic phantom assembly can be utilized for SPECT MP supine and, for first time, prone imaging to access and characterize artifacts due to different cranio-caudal respiratory amplitudes and cardiac-liver activity ratios.


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