Naveen Paluru, Aveen Dayal, Havard B. Jenssen, Tomas Sakinis, Linga R. Cenkeramaddi, Jaya Prakash, and Phaneendra K. Yalavarthy, “Anam-Net : Anamorphic Depth Embedding based Light-Weight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images,” IEEE Transactions on Neural Networks and Learning Systems (Fast Track: COVID-19 Focused Papers) 2021 (in press).
Keywords: COVID-19, Coronavirus, Deep Learning, Segmentation, and Abnormalities.
Abstract: Chest computed tomography (CT) imaging has become indispensable for staging and managing of COVID-19, and current evaluation of anomalies/abnormalities associated with COVID-19 has been performed majorly by visual score. The development of automated methods for quantifying COVID-19 abnormalities in these CT images is invaluable to clinicians. The hallmark of COVID-19 in chest CT images is the presence of ground-glass opacities in the lung region, which are tedious to segment manually. We propose anamorphic depth embedding based light-weight CNN, called Anam-Net, to segment anomalies in COVID-19 Chest CT images. The proposed Anam-Net has 7.8 times fewer parameters compared to the state-of-the-art UNet (or its variants), making it light-weight capable of providing inferences in mobile or resource constraint (point-of-care) platforms. The results from chest CT images (test cases) across different experiments showed that the proposed method could provide good Dice similarity scores for abnormal as well as normal regions in the lung. We have benchmarked Anam-Net with other state-of-the-art architectures like ENet, LEDNet, UNet++, SegNet, Attention UNet and DeepLabV3+. The proposed AnamNet was also deployed on embedded systems like Raspberry Pi 4, NVIDIA Jetson Xavier, and mobile based Android application (CovSeg) embedded with Anam-Net to demonstrate its suitability for point-of-care platforms. The generated codes, models, and the mobile application are available for enthusiastic users at https://github.com/NaveenPaluru/Segmentation-COVID-19.