Automatic MRI 2D Brain Segmentation using Graph SearchingTechnique

Accurate and efficient segmentation of the whole brain in magnetic resonance (MR) images is a key task in many neuroscience and medical studies either because the whole brain is the final anatomical structure of interest or because the automatic extraction facilitates further analysis. The problem of segmenting brain MRI images has been extensively addressed by many researchers. Despite the relevant achievements obtained, automated segmentation of brain MRI imagery is still a challenging problem whose solution has to cope with critical aspects such as anatomical variability and pathological deformation. In the present paper, we describe and experimentally evaluate a method for segmenting brain from MRI images basing on two-dimensional graph searching principles for border detection. The segmentation of the whole brain over the entire volume is accomplished slice by slice, automatically detecting frames including eyes. The method is fully automatic and easily reproducible by computing the internal main parameters directly from the image data. The segmentation procedure is conceived as a tool of general applicability, although design requirements are especially commensurate with the accuracy required in clinical tasks such as surgical planning and post-surgical assessment. Several experiments were performed to assess the performance of the algorithm on a varied set of MRI images obtaining good results in terms of accuracy and stabilit

Liver Segmentation and its Application to Hepatic Interventions

The thesis addresses the development of an intuitive and accurate liver segmentation approach, its integration into software prototypes for the planning of liver interventions, and research on liver regeneration. The developed liver segmentation approach is based on a combination of the live wire paradigm and shape-based interpolation. Extended with two correction modes and integrated into a user-friendly workflow, the method has been applied to more than 5000 data sets. The combination of the liver segmentation with image analysis of hepatic vessels and tumors allows for the computation of anatomical and functional remnant liver volumes. In several projects with clinical partners world-wide, the benefit of the computer-assisted planning was shown. New insights about the postoperative liver function and regeneration could be gained, and most recent investigations into the analysis of MRI data provide the option to further improve hepatic intervention planning

Multiatlas-Based Segmentation Editing With Interaction-Guided Patch Selection and Label Fusion

We propose a novel multi-atlas based segmentation method to address the segmentation editing scenario, where an incomplete segmentation is given along with a set of existing reference label images (used as atlases). Unlike previous multi-atlas based methods, which depend solely on appearance features, we incorporate interaction-guided constraints to find appropriate atlas label patches in the reference label set and derive their weights for label fusion. Specifically, user interactions provided on the erroneous parts are first divided into multiple local combinations. For each combination, the atlas label patches well-matched with both interactions and the previous segmentation are identified. Then, the segmentation is updated through the voxel-wise label fusion of selected atlas label patches with their weights derived from the distances of each underlying voxel to the interactions. Since the atlas label patches well-matched with different local combinations are used in the fusion step, our method can consider various local shape variations during the segmentation update, even with only limited atlas label images and user interactions. Besides, since our method does not depend on either image appearance or sophisticated learning steps, it can be easily applied to general editing problems. To demonstrate the generality of our method, we apply it to editing segmentations of CT prostate, CT brainstem, and MR hippocampus, respectively. Experimental results show that our method outperforms existing editing methods in all three data sets

Application of Solid Phase Microextraction for Quantitative Bioanalysis and Toxicokinetics: an Integrated Microsampling and Microanalysis Technique

Over the past decade, the growing field of microsampling has changed the way bioanalysis and preclinical studies are conducted. A variety of microsampling techniques have been adopted by the pharmaceutical industry and embedded into preclinical workflows. A technique known as solid phase microextraction (SPME) offers a distinctive advantage of measuring free drug concentrations within living organisms without the need for blood withdrawal. Despite its promise and potential advantages, SPME has not been extensively explored for preclinical use within the pharmaceutical industry. In this research, the application of SPME for quantitative bioanalysis and toxicokinetics was investigated for the first time within a pharmaceutical setting. This was performed through parallel in vitro and in vivo experiments. Initially three test compounds were selected (metoprolol, propranolol and diclofenac) and LC MS/MS methods were validated for all three. These were employed throughout the project to support quantitative analysis during the SPME in vitro and in vivo evaluation. SPME fibre blood exposure profiles and desorption profiles were constructed for the three tool compounds and parameters such as the impact of hematocrit levels, the effect of blood flow rate and on-fibre stability were investigated in vitro. SPME was then implemented in vivo. Practicalities of inserting the SPME fibre into the veins of animals was assessed using anesthetised rats and fibre blood exposure times were also determined during this first in vivo experiment. Since SPME measures free drug concentrations, its potential benefits as a tool to determine protein binding values of drugs were examined and compared to a gold standard approach for protein binding experiments known as rapid equilibrium dialysis (RED). The three tool analytes were studied as they cover a range of plasma protein binding levels (~ 30 - 99%) at three different physiologically relevant concentrations (10, 100 and 500 ng/mL). This was followed by an in vivo experiment to identify whether SPME measures free drug concentrations in conscious rats. In vivo SPME samples were compared with whole blood samples withdrawn from the same rats and analysed using the RED device. A full toxicology study was subsequently conducted in conscious rats for seven days to mimic a typical preclinical rodent study. SPME was compared with conventional caudal venipuncture whole blood sampling for generating toxicokinetic data. The impact and biocompatibility of SPME was studied through pathological endpoints and using an Irwin behavioural study. It was demonstrated that it may take up to 3 h for an analyte to reach equilibrium between the sample matrix and the SPME coating. This is not viable for in vivo applications due to ethical reasons and therefore pre-equilibrium conditions are more suited. Analyte desorption time of the SPME fibre was achieved between 15- 30 min. Levels of blood hematocrit had no impact on analyte response while blood flow rates may have an effect on analyte response and concentration. On-fibre stability was established for all three tool analytes for up to six weeks. It was found that consistent results were obtained by SPME when measuring protein binding values of all three analytes across three concentrations. The percentage difference between protein binding values determined by SPME and RED was within recommended limits for bioanalysis (<15 %) across all analytes and concentrations. The time required to obtain plasma protein values using SPME was considerably quicker than by using the RED device (1 h compared to 8 h). It was demonstrated that SPME provides a compelling alternative platform for the efficient generation of high quality plasma protein binding values. Pre-equilibrium conditions illustrated that using 2 min fibre exposure to systemic circulation was sufficient to produce reliable quantitative analysis. However, it was noted that current C18 fibre coatings did not detect metoprolol metabolite which exhibits a polar moiety. Mixed phase fibre coatings are required for metabolic analysis. The potential capacity of SPME to generate meaningful toxicokinetic data of free drug concentrations was shown. Biocompatibility of SPME was established by comparing pathological endpoints observed between SPME sampled and control rat groups. Finally, a novel approach was described for quantitative bioanalysis by direct SPME-MS. SPME was coupled to a mass spectrometer to enable direct elution of analytes from the SPME fibre onto the MS. This was characterised with two test analytes, metoprolol and propranolol, spiked into control rat blood. The data indicated the significance of this approach to enable rapid, selective and highly sensitive (10 ng/mL lower limit of quantification) qualitative and quantitative chemical analysis. Overall this research demonstrated that SPME could potentially provide a compelling alternative microsampling platform for preclinical studies

Semi-automated learning strategies for large-scale segmentation of histology and other big bioimaging stacks and volumes

Labelled high-resolution datasets are becoming increasingly common and necessary in different areas of biomedical imaging. Examples include: serial histology and ex-vivo MRI for atlas building, OCT for studying the human brain, and micro X-ray for tissue engineering. Labelling such datasets, typically, requires manual delineation of a very detailed set of regions of interest on a large number of sections or slices. This process is tedious, time-consuming, not reproducible and rather inefficient due to the high similarity of adjacent sections. In this thesis, I explore the potential of a semi-automated slice level segmentation framework and a suggestive region level framework which aim to speed up the segmentation process of big bioimaging datasets. The thesis includes two well validated, published, and widely used novel methods and one algorithm which did not yield an improvement compared to the current state-of the-art. The slice-wise method, SmartInterpol, consists of a probabilistic model for semi-automated segmentation of stacks of 2D images, in which the user manually labels a sparse set of sections (e.g., one every n sections), and lets the algorithm complete the segmentation for other sections automatically. The proposed model integrates in a principled manner two families of segmentation techniques that have been very successful in brain imaging: multi-atlas segmentation and convolutional neural networks. Labelling every structure on a sparse set of slices is not necessarily optimal, therefore I also introduce a region level active learning framework which requires the labeller to annotate one region of interest on one slice at the time. The framework exploits partial annotations, weak supervision, and realistic estimates of class and section-specific annotation effort in order to greatly reduce the time it takes to produce accurate segmentations for large histological datasets. Although both frameworks have been created targeting histological datasets, they have been successfully applied to other big bioimaging datasets, reducing labelling effort by up to 60−70% without compromising accuracy

A composite hydrogel for brain tissue phantoms

Synthetic phantoms are valuable tools for training, research and development in traditional and computer aided surgery, but complex organs, such as the brain, are difficult to replicate. Here, we present the development of a new composite hydrogel capable of mimicking the mechanical response of brain tissue under loading. Our results demonstrate how the combination of two different hydrogels, whose synergistic interaction results in a highly tunable blend, produces a hybrid material that closely matches the strongly dynamic and non-linear response of brain tissue. The new synthetic material is inexpensive, simple to prepare, and its constitutive components are both widely available and biocompatible. Our investigation of the properties of this engineered tissue, using both small scale testing and life-sized brain phantoms, shows that it is suitable for reproducing the brain shift phenomenon and brain tissue response to indentation and palpation