Preserving known anatomical topology in medical image segmentation using deep learning

Since the rise of deep learning, new medical image segmentation methods have rapidly been proposed with promising results, with each one reporting marginal improvements on the previous state-of-the-art (SOTA) method. However, on visual inspection, errors are often revealed, such as topological mistakes (e.g. holes or folds), that are not detected using traditional evaluation metrics, such as Dice. Moreover, correct topology is often essential in ensuring segmentations are anatomically and pathologically plausible and, ultimately, suitable for downstream image processing tasks. Therefore, there is a need for methods to focus on ensuring that the predicted segmentations are topologically correct, rather than just optimising the pixel-wise accuracy. In this thesis, I propose a method that utilises prior knowledge of anatomy to segment structures, whilst preserving the known topology. The presented model, Topology Encouraging Deformation Segmentation Network (TEDS-Net), performs segmentation by deforming a prior shape with the same topological features as the anatomy of interest using learnt topology-preserving deformation fields. However, here I show that such fields only guarantee topology preservation in the continuous domain and that their properties begin to break down when applied in discrete spaces. To overcome this effect, I introduced additional modifications in TEDS-Net to more strictly enforce topology preservation, a step often overlooked in previous work. Across this thesis, TEDS-Net is applied to a range of natural and medical image segmentation tasks. I show how it can be used for multiple topology types, multiple structures and in both two- and three-dimensions. Further, I show how TEDS-Net can be used to segment whole volumes using minimally annotated training data. Across these experiments, TEDS-Net outperforms all SOTA baselines on topology, whilst maintaining competitive pixel-wise accuracy. Finally, TEDS-Net is integrated into a whole medical imaging pipeline, to illustrate the importance of topologically correct segmentations for downstream tasks. TEDS-Net is used to segment the developing cortical plate from in-utero fetal brain ultrasound scans in 3D, to enable the characterisation of its complex growth and development during gestation. To the best of my knowledge, this task has only been previously attempted from magnetic resonance imaging (MRI), despite ultrasound being the modality of choice in prenatal care. This is likely due to large acoustic shadows obstructing key brain regions in ultrasound. Due to TEDS-Net anatomical constraints, it can anatomically guide the cortical plate segmentation in regions of shadows, producing a complete segmentation that enables accurate downstream analysis

Echocardiography

The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography

Probabilistic Models for Joint Segmentation, Detection and Tracking

Migrace buněk a buněčných částic hraje důležitou roli ve fungování živých organismů. Systematický výzkum buněčné migrace byl umožněn v posledních dvaceti letech rychlým rozvojem neinvazivních zobrazovacích technik a digitálních snímačů. Moderní zobrazovací systémy dovolují studovat chování buněčných populací složených z mnoha ticíců buněk. Manuální analýza takového množství dat by byla velice zdlouhavá, protože některé experimenty vyžadují analyzovat tvar, rychlost a další charakteristiky jednotlivých buněk. Z tohoto důvodu je ve vědecké komunitě velká poptávka po automatických metodách.Migration of cells and subcellular particles plays a crucial role in many processes in living organisms. Despite its importance a systematic research of cell motility has only been possible in last two decades due to rapid development of non-invasive imaging techniques and digital cameras. Modern imaging systems allow to study large populations with thousands of cells. Manual analysis of the acquired data is infeasible, because in order to gain insight into underlying biochemical processes it is sometimes necessary to determine shape, velocity and other characteristics of individual cells. Thus there is a high demand for automatic methods

Biological image analysis

In biological research images are extensively used to monitor growth, dynamics and changes in biological specimen, such as cells or plants. Many of these images are used solely for observation or are manually annotated by an expert. In this dissertation we discuss several methods to automate the annotating and analysis of bio-images. Two large clusters of methods have been investigated and developed. A first set of methods focuses on the automatic delineation of relevant objects in bio-images, such as individual cells in microscopic images. Since these methods should be useful for many different applications, e.g. to detect and delineate different objects (cells, plants, leafs, ...) in different types of images (different types of microscopes, regular colour photographs, ...), the methods should be easy to adjust. Therefore we developed a methodology relying on probability theory, where all required parameters can easily be estimated by a biologist, without requiring any knowledge on the techniques used in the actual software. A second cluster of investigated techniques focuses on the analysis of shapes. By defining new features that describe shapes, we are able to automatically classify shapes, retrieve similar shapes from a database and even analyse how an object deforms through time

A method to improve the computational efficiency of the Chan-Vese model for the segmentation of ultrasound images

Purpose Advanced image segmentation techniques like the Chan-Vese (CV) models transform the segmentation problem into a minimization problem which is then solved using the gradient descent (GD) optimization algorithm. This study explores whether the computational efficiency of CV can be improved when GD is replaced by a different optimization method. Methods Two GD variants from the literature (Nesterov accelerated, Barzilai-Borwein) and a newly developed hybrid variant of GD were used to improve the computational efficiency of CV by making GD insensitive to local minima. One more variant of GD from the literature (projected GD) was used to address the issue of maintaining the constraint on boundary evolution in CV which also increases computational cost. A novel modified projected GD (Barzilai-Borwein projected GD) was also used to overcome both problems at the same time. The effect of optimization method selection on processing time and the quality of the output was assessed for 25 musculoskeletal ultrasound images (five anatomical areas). Results The Barzilai-Borwein projected GD method was able to significantly reduce computational time (average(±std.dev.) reduction 95.82 % (±3.60 %)) with the least structural distortion in the delineated output relative to the conventional GD (average(±std.dev.) structural similarity index: 0.91(±0.06)). Conclusion The use of an appropriate optimization method can substantially improve the computational efficiency of CV models. This can open the way for real-time delimitation of anatomical structures to aid the interpretation of clinical ultrasound. Further research on the effect of the optimization method on the accuracy of segmentation is needed