Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images towards Achieving Label-free Angiography

Automated segmentation of retinal blood vessels in label-free fundus images entails a pivotal role in computed aided diagnosis of ophthalmic pathologies, viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases. The challenge remains active in medical image analysis research due to varied distribution of blood vessels, which manifest variations in their dimensions of physical appearance against a noisy background. In this paper we formulate the segmentation challenge as a classification task. Specifically, we employ unsupervised hierarchical feature learning using ensemble of two level of sparsely trained denoised stacked autoencoder. First level training with bootstrap samples ensures decoupling and second level ensemble formed by different network architectures ensures architectural revision. We show that ensemble training of auto-encoders fosters diversity in learning dictionary of visual kernels for vessel segmentation. SoftMax classifier is used for fine tuning each member auto-encoder and multiple strategies are explored for 2-level fusion of ensemble members. On DRIVE dataset, we achieve maximum average accuracy of 95.33\% with an impressively low standard deviation of 0.003 and Kappa agreement coefficient of 0.708 . Comparison with other major algorithms substantiates the high efficacy of our model.Comment: Accepted as a conference paper at IEEE EMBC, 201

Self Supervised Low Dose Computed Tomography Image Denoising Using Invertible Network Exploiting Inter Slice Congruence

The resurgence of deep neural networks has created an alternative pathway for low-dose computed tomography denoising by learning a nonlinear transformation function between low-dose CT (LDCT) and normal-dose CT (NDCT) image pairs. However, those paired LDCT and NDCT images are rarely available in the clinical environment, making deep neural network deployment infeasible. This study proposes a novel method for self-supervised low-dose CT denoising to alleviate the requirement of paired LDCT and NDCT images. Specifically, we have trained an invertible neural network to minimize the pixel-based mean square distance between a noisy slice and the average of its two immediate adjacent noisy slices. We have shown the aforementioned is similar to training a neural network to minimize the distance between clean NDCT and noisy LDCT image pairs. Again, during the reverse mapping of the invertible network, the output image is mapped to the original input image, similar to cycle consistency loss. Finally, the trained invertible network's forward mapping is used for denoising LDCT images. Extensive experiments on two publicly available datasets showed that our method performs favourably against other existing unsupervised methods.Comment: 10 pages, Accepted in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 202

Sustainability Assessment of a Residential Building using a Life Cycle Assessment Approach

Building and construction industry is responsible for resource scarcity, global warming impacts, land use changes and the loss of bio-diversity, which have direct and indirect socio-economic implications. Sustainable building design is thus inevitable through the selection of highly durable and less energy intensive-materials that could reduce environmental degradation in an economically viable and socially acceptable manner. This paper presents the life cycle sustainability assessment (LCSA) framework to assess the environmental, social and economic objectives of residential buildings. Two buildings of different material compositions have been used to test this framework. Firstly, the service life of this building has been calculated as durability of building materials play a key role in enhancing resource conservation for the future generations. A factor method has been used to carry out the service life of each component of the building envelope. The minimum estimated service life of building systems is considered as the overall service life of building components. Secondly, a life cycle assessment framework utilising environmental life cycle assessment, life cycle costing and social life cycle assessment have been utilised to determine environmental, economic and social indicators of the studied buildings. All these triple bottom line indicators in this framework have been calculated on an annual basis in order to capture the advantage of increased service life of buildings. This framework will be applied to assess the sustainability performance of alternative buildings for comparative analysis and to find out the most sustainable building option