Segmentation of infected region in CT images of COVID-19 patients based on QC-HC U-net

Posted: 2021-11-25 20:00:00
Since the outbreak of COVID-19 in 2019, the rapid spread of the epidemic has brought huge challenges to medical institutions. If the pathological region in the COVID-19 CT image can be automatically segmented, it will help doctors quickly determine the patient's infection, thereby speeding up the diagnosis process. To be able to automatically segment the infected area, we proposed a new network structure and named QC-HC U-Net. First, we combine residual connection and dense connection to form a new connection method and apply it to the encoder and the decoder. Second, we choose to add Hypercolumns in the decoder section. Compared with the benchmark 3D U-Net, the improved network can effectively avoid vanishing gradient while extracting more features. To improve the situation of insufficient data, resampling and data enhancement methods are selected in this paper to expand the datasets. We used 63 cases of MSD lung tumor data for training and testing, continuously verified to ensure the training effect of this model, and then selected 20 cases of public COVID-19 data for training and testing. Experimental results showed that in the segmentation of COVID-19, the specificity and sensitivity were 85.3% and 83.6%, respectively, and in the segmentation of MSD lung tumors, the specificity and sensitivity were 81.45% and 80.93%, respectively, without any fitting.

参考サイト PubMed: covid-19

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12月 07, 2020 バイオアソシエイツ


2020年11月14日にThe Journal of Extracellular Vesiclesのオンラインで公開されたオープンアクセスの論文で、イェール大学医学部内科リウマチ学および臨床免疫学部門医学病理学教授のPhilip Askenase医学博士(写真)は、重度の COVID-19 患者の場合、間葉系幹細胞(MSC)由来の エキソソーム…

ゲスト 634人 と メンバー 6人 がオンラインです