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

Lightweight Modules for Efficient Deep Learning based Image Restoration

Low level image restoration is an integral component of modern artificial intelligence (AI) driven camera pipelines. Most of these frameworks are based on deep neural networks which present a massive computational overhead on resource constrained platform like a mobile phone. In this paper, we propose several lightweight low-level modules which can be used to create a computationally low cost variant of a given baseline model. Recent works for efficient neural networks design have mainly focused on classification. However, low-level image processing falls under the image-to-image' translation genre which requires some additional computational modules not present in classification. This paper seeks to bridge this gap by designing generic efficient modules which can replace essential components used in contemporary deep learning based image restoration networks. We also present and analyse our results highlighting the drawbacks of applying depthwise separable convolutional kernel (a popular method for efficient classification network) for sub-pixel convolution based upsampling (a popular upsampling strategy for low-level vision applications). This shows that concepts from domain of classification cannot always be seamlessly integrated into image-to-image translation tasks. We extensively validate our findings on three popular tasks of image inpainting, denoising and super-resolution. Our results show that proposed networks consistently output visually similar reconstructions compared to full capacity baselines with significant reduction of parameters, memory footprint and execution speeds on contemporary mobile devices.Comment: Accepted at: IEEE Transactions on Circuits and Systems for Video Technology (Early Access Print) | |Codes Available at: https://github.com/avisekiit/TCSVT-LightWeight-CNNs | Supplementary Document at: https://drive.google.com/file/d/1BQhkh33Sen-d0qOrjq5h8ahw2VCUIVLg/view?usp=sharin

Intensity modulated radiotherapy in carcinoma cervix with metastatic para-aortic nodes: an institutional experience from a Regional Cancer Centre of Eastern India

BACKGROUND: Cervical cancer is a major health problem, especially in developing countries like India. Extended field radiotherapy (EFRT) for cancer cervix treatment remains a challenging task for radiation oncologists. In the last decade studies have shown that EFRT using intensity modulated radiotherapy (IMRT) is feasible in treating gynaecological malignancies but there is a dearth of literature on this specific topic from this part of the world where patient profile differs greatly in several aspects from that of the western world. The aim of the study was evaluation of treatment response and toxicity profile in cases of carcinoma cervix with metastatic para-aortic nodes treated with intensity modulated radiotherapy technique. MATERIALS AND METHODS: In this retrospective study the treatment records of 45 para-aortic node positive cervical cancer patients treated with EFRT (IMRT) and concurrent cisplatin were analysed for evaluation of loco-regional control and toxicities. RESULTS: Forty-four patients received full course of treatment. Among those 44 patients, 93.2% achieved complete response. Overall, the treatment was tolerated well and toxicities were within acceptable limits. Acute grade 3-4 toxicities were observed mostly in the form of anaemia and leucopenia. Most common late toxicities were those of small and large intestine. CONCLUSION: EFRT with concurrent chemotherapy was successfully delivered for para-aortic nodes positive cervical cancer patients in Indian scenario where under-nutrition, infection, anaemia and several other factors adversely influence treatment outcome. Pelvic and para-aortic control rates were satisfactory. The technique was associated with an acceptable acute and late toxicity profile