scholarly journals A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis

2020 ◽  
Vol 56 (2) ◽  
pp. 2000775 ◽  
Author(s):  
Shuo Wang ◽  
Yunfei Zha ◽  
Weimin Li ◽  
Qingxia Wu ◽  
Xiaohu Li ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
High Risk ◽  
Learning System ◽  
Medical Resource ◽  
Early Prevention ◽  
Prognostic Analysis ◽  
Computed Tomography Images ◽  
Risk Patients ◽  
Fully Automatic

Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from seven cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the deep learning system, making it learn lung features. Following this, 1266 patients (924 with COVID-19 (471 had follow-up for >5 days) and 342 with other pneumonia) from six cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the four external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC 0.87 and 0.88, respectively) and viral pneumonia (AUC 0.86). Moreover, the deep learning system succeeded to stratify patients into high- and low-risk groups whose hospital-stay time had significant difference (p=0.013 and p=0.014, respectively). Without human assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening of COVID-19 and identifying potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.

scholarly journals A Fully Automatic Deep Learning System for COVID-19 Diagnostic and Prognostic Analysis

2020 ◽  
Author(s):  
Shuo Wang ◽  
Yunfei Zha ◽  
Weimin Li ◽  
Qingxia Wu ◽  
Xiaohu Li ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
High Risk ◽  
Learning System ◽  
High Risk Patients ◽  
Prognostic Analysis ◽  
Computed Tomography Images ◽  
Risk Patients ◽  
Fully Automatic ◽  
Worse Prognosis

AbstractCoronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19, and finding high-risk patients with worse prognosis for early prevention and medical resources optimization is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from 7 cities or provinces. Firstly, 4106 patients with computed tomography images and gene information were used to pre-train the DL system, making it learn lung features. Afterwards, 1266 patients (924 with COVID-19, and 471 had follow-up for 5+ days; 342 with other pneumonia) from 6 cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the 4 external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC=0.87 and 0.88) and viral pneumonia (AUC=0.86). Moreover, the deep learning system succeeded to stratify patients into high-risk and low-risk groups whose hospital-stay time have significant difference (p=0.013 and 0.014). Without human-assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening COVID-19 and finding potential high-risk patients, which may be helpful for medical resource optimization and early prevention before patients show severe symptoms.Take-home messageFully automatic deep learning system provides a convenient method for COVID-19 diagnostic and prognostic analysis, which can help COVID-19 screening and finding potential high-risk patients with worse prognosis.

Liver segmentation from computed tomography images using cascade deep learning

2021 ◽  
pp. 105095
Author(s):  
José Denes Lima Araújo ◽  
Luana Batista da Cruz ◽  
João Otávio Bandeira Diniz ◽  
Jonnison Lima Ferreira ◽  
Aristófanes Corrêa Silva ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Liver Segmentation ◽  
Computed Tomography Images

A Fully Automatic Deep Learning System for L3 Slice Selection and Body Composition Assessment on Abdominal Computed Tomography. (Preprint)

2021 ◽  
Author(s):  
Jiyeon Ha ◽  
Taeyong Park ◽  
Hong-Kyu Kim ◽  
Youngbin Shin ◽  
Yousun Ko ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
External Validation ◽  
Abdominal Muscle ◽  
Anatomic Variations ◽  
Success Rates ◽  
Technical Success ◽  
Segmentation Accuracy ◽  
Distance Difference ◽  
Fully Automatic

BACKGROUND As sarcopenia research has been gaining emphasis, the need for quantification of abdominal muscle on computed tomography (CT) is increasing. Thus, a fully automated system to select L3 slice and segment muscle in an end-to-end manner is demanding. OBJECTIVE We aimed to develop a deep learning model (DLM) to select the L3 slice with consideration of anatomic variations and to segment cross-sectional areas (CSAs) of abdominal muscle and fat. METHODS Our DLM, named L3SEG-net, was composed of a YOLOv3-based algorithm for selecting the L3 slice and a fully convolutional network (FCN)-based algorithm for segmentation. The YOLOv3-based algorithm was developed via supervised learning using a training dataset (n=922), and the FCN-based algorithm was transferred from prior work. Our L3SEG-net was validated with internal (n=496) and external validation (n=586) datasets. L3 slice selection accuracy was evaluated by the distance difference between ground truths and DLM-derived results. Technical success for L3 slice selection was defined when the distance difference was <10 mm. Overall segmentation accuracy was evaluated by CSA error. The influence of anatomic variations on DLM performance was evaluated. RESULTS In the internal and external validation datasets, the accuracy of automatic L3 slice selection was high, with mean distance differences of 3.7±8.4 mm and 4.1±8.3 mm, respectively, and with technical success rates of 93.1% and 92.3%, respectively. However, in the subgroup analysis of anatomic variations, the L3 slice selection accuracy decreased, with distance differences of 12.4±15.4 mm and 12.1±14.6 mm, respectively, and with technical success rates of 67.2% and 67.9%, respectively. The overall segmentation accuracy of abdominal muscle areas was excellent regardless of anatomic variation, with the CSA errors of 1.38–3.10 cm2. CONCLUSIONS A fully automatic system was developed for the selection of an exact axial CT slice at the L3 vertebral level and the segmentation of abdominal muscle areas.

scholarly journals A fully automatic deep learning system for L3 slice selection and body composition assessment on abdominal computed tomography. 

2021 ◽  
Author(s):  
Jiyeon Ha ◽  
Taeyong Park ◽  
Hong-Kyu Kim ◽  
Youngbin Shin ◽  
Yousun Ko ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
External Validation ◽  
Abdominal Muscle ◽  
Anatomic Variations ◽  
Success Rates ◽  
Technical Success ◽  
Segmentation Accuracy ◽  
Distance Difference ◽  
Fully Automatic

Abstract Background and aims: As sarcopenia research has been gaining emphasis, the need for quantification of abdominal muscle on computed tomography (CT) is increasing. Thus, a fully automated system to select L3 slice and segment muscle in an end-to-end manner is demanded. We aimed to develop a deep learning model (DLM) to select the L3 slice with consideration of anatomic variations and to segment cross-sectional areas (CSAs) of abdominal muscle and fat. Methods: Our DLM, named L3SEG-net, was composed of a YOLOv3-based algorithm for selecting the L3 slice and a fully convolutional network (FCN)-based algorithm for segmentation. The YOLOv3-based algorithm was developed via supervised learning using a training dataset (n=922), and the FCN-based algorithm was transferred from prior work. Our L3SEG-net was validated with internal (n=496) and external validation (n=586) datasets. L3 slice selection accuracy was evaluated by the distance difference between ground truths and DLM-derived results. Technical success for L3 slice selection was defined when the distance difference was <10 mm. Overall segmentation accuracy was evaluated by CSA error. The influence of anatomic variations on DLM performance was evaluated.Results: In the internal and external validation datasets, the accuracy of automatic L3 slice selection was high, with mean distance differences of 3.7±8.4 mm and 4.1±8.3 mm, respectively, and with technical success rates of 93.1% and 92.3%, respectively. However, in the subgroup analysis of anatomic variations, the L3 slice selection accuracy decreased, with distance differences of 12.4±15.4 mm and 12.1±14.6 mm, respectively, and with technical success rates of 67.2% and 67.9%, respectively. The overall segmentation accuracy of abdominal muscle areas was excellent regardless of anatomic variation, with the CSA errors of 1.38–3.10 cm2.Conclusions: A fully automatic system was developed for the selection of an exact axial CT slice at the L3 vertebral level and the segmentation of abdominal muscle areas.

scholarly journals Deep Learning Based Liver Cancer Segmentation from Computed Tomography Images

2020 ◽  
Author(s):  
Yodit Abebe Ayalew ◽  
Kinde Anlay Fante ◽  
Mohammed Aliy
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Liver Cancer ◽  
Ct Scan ◽  
Tumor Segmentation ◽  
Cancer Death ◽  
Liver Segmentation ◽  
Abdominal Ct ◽  
Computed Tomography Images ◽  
Abdominal Ct Scan

Abstract Background: Liver cancer is the sixth most common cancer worldwide. According to WHO data in 2017, the liver cancer death in Ethiopia reached 1040 (0.16%) from all cancer deaths. Hepatocellular carcinoma (HCC), primary liver cancer causes the death of around 700,000 people each year worldwide and this makes it the third leading cause of cancer death. HCC is occurred due to cirrhosis and hepatitis B or C viruses. Liver cancer mostly diagnosed with a computed tomography (CT) scan. But, the detection of the tumor from the CT scan image is difficult since tumors have similar intensity with nearby tissues and may have a different appearance depending on their type, state, and equipment setting. Nowadays deep learning methods have been used for the segmentation of liver and its tumor from the CT scan images and they are more efficient than those traditional methods. But, they are computationally expensive and need many labeled samples for training, which are difficult in the case of biomedical images. Results: A deep learning-based segmentation algorithm is employed for liver and tumor segmentation from abdominal CT scan images. Three separate UNet models, one for liver segmentation and the others two for tumor segmentation from the segmented liver and directly from the abdominal CT scan image were used. A dice score of 0.96 was obtained for liver segmentation. And a dice score of 0.74 and 0.63 was obtained for segmentation of tumor from the liver and from abdominal CT scan image respectively. Conclusion: The research improves the liver tumor segmentation that will help the physicians in the diagnosis and detection of liver tumors and in designing a treatment plan for the patient. And for the patient, it increases the patients’ chance of getting treatment and decrease the mortality rate due to liver cancer.

scholarly journals Prognostic analysis of DLBCL patients and the role of upfront ASCT in high-intermediate and high-risk patients

Oncotarget ◽  
2017 ◽  
Vol 8 (42) ◽  
pp. 73168-73176 ◽  
Author(s):  
Ying Zhao ◽  
Hong Wang ◽  
Song Jin ◽  
Jiajia Zheng ◽  
Man Huang ◽  
...  
Keyword(s):  
High Risk ◽  
High Risk Patients ◽  
Prognostic Analysis ◽  
Risk Patients
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