Dissertation and Thesis
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Item Few shot segmentation for COVID-19 infected lung CT slices(Indian Statistical Institute, Kolkata, 2022-07) Chatterjee, SohamThe last 2 years have been adversely affected by the COVID-19 pandemic. Doctors usually detect Covid from CT slices from features such as ground glass, consolidation and pleural effusion. These features usually have complex contours, irregular shapes and rough boundaries. With increasing number of cases the workload on the radiologists have increased by leaps and bounds to analyze the lung CT scans for tracking the disease progression in the patient. Moreover manual analysis of the CT scans is also prone to human error. So automated segmentation of infected lung CT slices can help the doctors to diagnose the disease faster. With the advent of deep learning, various approaches have been built to tackle this problem of automated biomedical image segmentation. One such architecture is the U-Net by Ronnenberger et al. [14]. Various other approaches have been proposed which are all variations of the U-Net to achieve better segmentation performance. However, the U-Net and its variations suffer from high model complexity, due to which they easily overfit on limited labelled dataset which is a serious issue in medical image domain. To cater this problem of data scarcity, research in “few shot segmentation” has gained significant importance in the recent years. In this work, we have developed a deep neural network model called Few Shot Conditioner Segmenter Covid (FSCS-cov), an architecture to tackle the problem of segmenting different COVID- 19 lesions from limited number of COVID-19 infected lung CT slices using few - shot learning paradigm.Item Deep learning for COVID-19 lung pathology segmentation(Indian Statistical Institute, Kolkata., 2021-07) Bedi, Gurdit SinghCOVID-19 pandemic has impacted billions of lives and created a challenge for the healthcare systems. Detection of pathologies from computed tomography (CT) images offers a great way to assist the traditional healthcare for tackling COVID-19. Pathologies such as ground-glass opacification and consolidations are region of interests which the doctors use to diagnosis the patients. In this work, we have developed and tested various segmentation model using transfer learning to find such pathologies. U-Net [15] is the foundation of the models which we have tested. Along with U-Net we have changed the encoder section of the said model, to various classification models such as VGG, ResNet and MobileNet. As these model have won ImageNet Challenge, there core component have been used for feature extraction and usage of their pretrained weights will help in faster convergence. A small subset of studies which has been annotated with binary pixel masks depicting regions of interests in MosMedData [12] Chest CT Scans dataset have been used to train the segmentation model. The best segmentation model achieved a mean dice score of 0.6029.