MorphPool: Efficient Non-linear Pooling & Unpooling in CNNs

Pooling is essentially an operation from the field of Mathematical Morphology, with max pooling as a limited special case. The more general setting of MorphPooling greatly extends the tool set for building neural networks. In addition to pooling operations, encoder-decoder networks used for pixel-level predictions also require unpooling. It is common to combine unpooling with convolution or deconvolution for up-sampling. However, using its morphological properties, unpooling can be generalised and improved. Extensive experimentation on two tasks and three large-scale datasets shows that morphological pooling and unpooling lead to improved predictive performance at much reduced parameter counts.Comment: Accepted paper at the British Machine Vision Conference (BMVC) 202

Deep Retinal Optical Flow: From Synthetic Dataset Generation to Framework Creation and Evaluation

Sustained delivery of regenerative retinal therapies by robotic systems requires intra-operative tracking of the retinal fundus. This thesis presents a supervised convolutional neural network to densely predict optical flow of the retinal fundus, using semantic segmentation as an auxiliary task. Retinal flow information missing due to occlusion by surgical tools or other effects is implicitly inpainted, allowing for the robust tracking of surgical targets. As manual annotation of optical flow is infeasible, a flexible algorithm for the generation of large synthetic training datasets on the basis of given intra-operative retinal images and tool templates is developed. The compositing of synthetic images is approached as a layer-wise operation implementing a number of transforms at every level which can be extended as required, mimicking the various phenomena visible in real data. Optical flow ground truth is calculated from motion transforms with the help of oflib, an open-source optical flow library available from the Python Package Index. It enables the user to manipulate, evaluate, and combine flow fields. The PyTorch version of oflib is fully differentiable and therefore suitable for use in deep learning methods requiring back-propagation. The optical flow estimation from the network trained on synthetic data is evaluated using three performance metrics obtained from tracking a grid and sparsely annotated ground truth points. The evaluation benchmark consists of a series of challenging real intra-operative clips obtained from an extensive internally acquired dataset encompassing representative surgical cases. The deep learning approach clearly outperforms variational baseline methods and is shown to generalise well to real data showing scenarios routinely observed during vitreoretinal procedures. This indicates complex synthetic training datasets can be used to specifically guide optical flow estimation, laying the foundation for a robust system which can assist with intra-operative tracking of moving surgical targets even when occluded

Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement

Burst image processing is becoming increasingly popular in recent years. However, it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghosting and zipper artifacts. Existing burst restoration methods usually do not consider the mutual correlation and non-local contextual information among burst frames, which tends to limit these approaches in challenging cases. Another key challenge lies in the robust up-sampling of burst frames. The existing up-sampling methods cannot effectively utilize the advantages of single-stage and progressive up-sampling strategies with conventional and/or recent up-samplers at the same time. To address these challenges, we propose a novel Gated Multi-Resolution Transfer Network (GMTNet) to reconstruct a spatially precise high-quality image from a burst of low-quality raw images. GMTNet consists of three modules optimized for burst processing tasks: Multi-scale Burst Feature Alignment (MBFA) for feature denoising and alignment, Transposed-Attention Feature Merging (TAFM) for multi-frame feature aggregation, and Resolution Transfer Feature Up-sampler (RTFU) to up-scale merged features and construct a high-quality output image. Detailed experimental analysis on five datasets validates our approach and sets a state-of-the-art for burst super-resolution, burst denoising, and low-light burst enhancement.Comment: Accepted at CVPR 202

NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this application requires substantial implementation effort. Thus, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D and 3D images and computational graphs by default. We present 3 illustrative medical image analysis applications built using NiftyNet: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6 figures; Update includes additional applications, updated author list and formatting for journal submissio