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Scar Segmentation in CMR Images without Using Contrast Agent
, M.Sc. Thesis Sharif University of Technology ; Rohban, Mohammad Hossein (Supervisor) ; Houshmand, Golnaz (Co-Supervisor)
Abstract
Correct diagnosis of myocardial scar has always been a major challenge due to the low resolution of cardiac magnetic resonance imaging. The use of a gadolinium-based contrast agent that reveals a scar is the solution proposed in medical science. However, there are limitations to the use of contrast agents in some patients. In recent years, studies based on deep learning techniques have been presented, trying to identify myocardial infarction with the help of images without using contrast agent. This type of diagnosis can be done with the help of different movements of healthy and damaged tissue. Due to the lack of datasets suitable for this application, in this study, real dataset were...
Crack Detection of Asphalt Concrete Pavements Based on Deep Learning
, M.Sc. Thesis Sharif University of Technology ; Sabouri, Mohammad Reza (Supervisor)
Abstract
The health of the pavement ensures the safety and convenience of drivers and passengers. In the past few decades, pavement management systems have encountered challenges that often have produced solutions with excessive demand for resources, but low-accuracy results. New approaches must be developed in order to quickly and economically identify pavement failure, especially cracks. In recent years, researchers have focused on identifying pavement failures, but previous methods only worked on images that solely included pavements and cracks. However, when foreign objects such as cars and vegetation were present, these methods were not as effective. To improve upon these methods, semantic...
Optimized U-shape convolutional neural network with a novel training strategy for segmentation of concrete cracks
, Article Structural Health Monitoring ; 2022 ; 14759217 (ISSN) ; Bakhshi, A ; Sharif University of Technology
SAGE Publications Ltd
2022
Abstract
Crack detection is a vital component of structural health monitoring. Several computer vision-based studies have been proposed to conduct crack detection on concrete surfaces, but most cases have difficulties in detecting fine cracks. This study proposes a deep learning-based model for automatic crack detection on the concrete surface. Our proposed model is an encoder–decoder model which uses EfficientNet-B7 as the encoder and U-Net’s modified expansion path as the decoder. To overcome the challenges in the detection of fine cracks, we trained our model with a new training strategy on images extracted from an open-access dataset and achieved a 96.98% F1 score for unseen test data. Moreover,...
Bi-directional ConvLSTM U-net with densley connected convolutions
, Article 17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019, 27 October 2019 through 28 October 2019 ; 2019 , Pages 406-415 ; 9781728150239 (ISBN) ; Asadi Aghbolaghi, M ; Fathy, M ; Escalera, S ; Computer Vision Foundation; IEEE ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional...