Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images

Abstract

Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-theart performance.

Segmentation and Evaluation of COVID-19 Lesion from CT scan Slices - A Study with Kapur/Otsu Function and Cuckoo Search Algorithm

“…The existing methods include; (i) Segmentation techniques, (ii) Machine-Learning-Schemes (MLS) and (iii) Deep-Learning-Systems (DLS) [11,12]. The existing methods are implemented on a class of real time and benchmark images and validated using a chosen software platforms The earlier works in the literature also confirms that, the assessment of the COVID-19 disease using a chosen image processing scheme substantially reduces the burden and helps to implement the treatment implementation process [13][14][15][16].…”

“…Due to its infection rate and harshness, the COVID-19 infected patients are kept in isolated and controlled surroundings and when the infection rate increases, the patient count also increases; which will increase the burden of the doctors and the pulmonologist. To reduce the diagnostic burden; recently a number of automated pneumonia detection system using CTS/Chest X-ray was proposed by the researchers [15,16]. The existing methods include; (i) Segmentation techniques, (ii) Machine-Learning-Schemes (MLS) and (iii) Deep-Learning-Systems (DLS) [11,12].…”

SARS-CoV-2: Enhancement and Segmentation of High-ResolutionMicroscopy Images. Part I

“…The fight against COVID-19 has motivated many scientific institutions and researchers from all the specials in the world to seek effective methods and techniques to help end this pandemic. In this direction, the computer vision community has not been left behind, and many articles have been published, mainly for the improvement of Computed Tomography (CT) images using deep learning [3,4,5,6,7]. ______________________________________ However, it draws attention the small number of published papers for the improvement of microscopic images, when in known the blurring problems inherent in these types of images, which often makes it difficult to analyze correctly them.…”

Clinical deployment and validation of a radiology artificial intelligence system for COVID-19

“…To mitigate the burden on radiologists, while providing the highest quality care for patients, there has been tremendous effort to develop novel image processing approaches using machine learning algorithms 24 , particularly for COVID-19 diagnosis and prognosis 25 . These artificial intelligence (AI) models exploit and build upon medical imaging modalities such as chest CT scans [26][27][28][29][30][31][32] , chest radiographs [33][34][35][36][37][38][39][40] , and lung ultrasound 41 However, for any of these AI models to be useful in assisting clinicians in the care of COVID-19 patients, they require a robust and reliable AI deployment system 42 . Deployment is often a difficult step because clinical radiology infrastructure is not designed for easily embedding third-party systems, and doing so while maintaining context sensitivity and seamlessly embedding such systems into the radiologist workflow requires knowledge of hospital information system integration standards and often product-specific knowledge.…”