TotalSegmentator: robust segmentation of 104 anatomical structures in CT images

We present a deep learning segmentation model that can automatically and robustly segment all major anatomical structures in body CT images. In this retrospective study, 1204 CT examinations (from the years 2012, 2016, and 2020) were used to segment 104 anatomical structures (27 organs, 59 bones, 10 muscles, 8 vessels) relevant for use cases such as organ volumetry, disease characterization, and surgical or radiotherapy planning. The CT images were randomly sampled from routine clinical studies and thus represent a real-world dataset (different ages, pathologies, scanners, body parts, sequences, and sites). The authors trained an nnU-Net segmentation algorithm on this dataset and calculated Dice similarity coefficients (Dice) to evaluate the model's performance. The trained algorithm was applied to a second dataset of 4004 whole-body CT examinations to investigate age dependent volume and attenuation changes. The proposed model showed a high Dice score (0.943) on the test set, which included a wide range of clinical data with major pathologies. The model significantly outperformed another publicly available segmentation model on a separate dataset (Dice score, 0.932 versus 0.871, respectively). The aging study demonstrated significant correlations between age and volume and mean attenuation for a variety of organ groups (e.g., age and aortic volume; age and mean attenuation of the autochthonous dorsal musculature). The developed model enables robust and accurate segmentation of 104 anatomical structures. The annotated dataset (https://doi.org/10.5281/zenodo.6802613) and toolkit (https://www.github.com/wasserth/TotalSegmentator) are publicly available.Comment: Accepted at Radiology: Artificial Intelligenc

The investigation of hippocampal and hippocampal subfield volumetry, morphology and metabolites using 3T MRI

A detailed account of the hippocampal anatomy has been provided. This thesis will explore and exploit the use of 3T MRI and the latest developments in image processing techniques to measure hippocampal and hippocampal subfield volumes, hippocampal metabolites and morphology. In chapter two a protocol for segmenting the hippocampus was created. The protocol was assessed in two groups of subjects with differing socioeconomic status (SES). This was a novel, community based sample in which hippocampal volumes have yet to be assessed in the literature. Manual and automated hippocampal segmentation measurements were compared on the two distinct SES groups. The mean volumes and also the variance in these measurements were comparable between two methods. The Dice overlapping metric comparing the two methods was 0.81. In chapter three voxel based morphometry (VBM) was used to compare local volume differences in grey matter volume between the two SES groups. Two approaches to VBM were compared. DARTEL-VBM results were found to be superior to the earlier ’optimised’ VBM method. Following a small volume correction, DARTEL-VBM results were suggesitive of focal GM volumes reductions in both the right and left hippocampi of the lower SES group. In chapter four an MR spectroscopy protocol was implemented to assess hippocampal metabolites in the two differing SES groups. Interpretable spectra were obtained in 73% of the 42 subjects. The poorer socioeconomic group were considered to have been exposed to chronic stress and therefore via inflammatory processes it was anticipated that the NAA/Cr metabolite ratio would be reduced in this group when compared to the more affluent group. Both NAA/Cr and Cho/Cr hippocampal metabolite ratios were not significantly different between the two groups. The aim of chapter 5 was to implement the protocol and methodology developed in chapter 2 to determine a normal range for hippocampal volumes at 3T MRI. 3D T1-weighted IR-FSPGR images were acquired in 39 healthy, normal volunteers in the age range from 19 to 64. Following the automated procedure hippocampal volumes were manually inspected and edited. The mean and standard deviation of the left and right hippocampal volumes were determined to be: 3421mm3 ± 399mm3 and 3487mm3 ± 431mm3 respectively. After correcting for total ICV the volumes were: 0.22% ± 0.03% and 0.23% ± 0.03% for the left and right hippocampi respectively. Thus, a normative database of hippocampal volumes was established. The normative data here will in future act as a baseline on which other methods of determining hippocampal volumes may be compared. The utility of using the normative dataset to compare other groups of subjects will be limited as a result of the lack of a comprehensive assessment of IQ or education level of the normal volunteers which may affect the volume of the hippocampus. In chapter six Incomplete hippocampal inversion (IHI) was assessed. Few studies have assessed the normal incidence of IHI and of those studies the analysis of IHI extended only to a radiological assessment. Here we present a comprehensive and quantitative assessment of IHI. IHI was found on 31 of the 84 normal subjects assessed (37%). ICV corrected IHI left-sided hippocampal volumes were compared against ICV corrected normal left-sided hippocampal volumes (25 vs. 52 hippocampi). The IHI hippocampal volumes were determined to be smaller than the normal hippocampal volumes (p<< 0.05). However, on further inspection it was observed that the ICV of the IHI was significantly smaller than the ICV of the normal group, confounding the previous result. In chapter seven a pilot study was performed on patients with Rheumatoid Arthritis (RA). The aim was to exploit the improved image quality offered by the 3T MRI to create a protocol for assessing the CA4/ dentate volume and to compare the volume of this subfield of the hippocampus before and after treatment. Two methodologies were implemented. In the first method a protocol was produced to manually segment the CA4/dentate region of the hippocampus from coronal T2-weighted FSE images. Given that few studies have assessed hippocampal subfields, an assessment of study power and sample size was conducted to inform future work. In the second method, the data the DARTEL-VBM image processing pipeline was applied. Statistical nonparametric mapping was applied in the final statistical interpretation of the VBM data. Following an FDR correction, a single GM voxel in the hippocampus was deemed to be statistically significant, this was suggestive of small GM volume increase following antiinflammatory treatment. Finally, in chapter eight, the manual segmentation protocol for the CA4/dentate hippocampal subfield developed in chapter seven was extended to include a complete set of hippocampal subfields. This is one of the first attempts to segment the entire hippocampus into its subfields using 3T MRI and as such, it was important to assess the quality of the measurement procedure. Furthermore, given the subfield volumes and the variability in these measurements, power and sample size calculations were also estimated to inform further work. Seventeen healthy volunteers were scanned using 3T MRI. A detailed manual segmentation protocol was created to guide two independent operators to measure the hippocampal subfield volumes. Repeat measures were made by a single operator for intra-operator variability and inter-operator variability was also assessed. The results of the intra-operator comparison proved reasonably successful where values compared well but were typically slightly poorer than similar attempts in the literature. This was likely to be the result of the additional complication of trying to segment subfields in the head and tail of the hippocampus where previous studies have focused only on the body of the hippocampus. Inter-rater agreement measures for subfield volumes were generally poorer than would be acceptable if full exchangeability of the data between the raters was necessary. This would indicate that further refinements to the manual segmentation protocol are necessary. Future work should seek to improve the methodology to reduce the variability and improve the reproducibility in these measures

Volumetric Assessment Of Imaging Response In The Pnoc Pediatric Glioma Clinical Trials

Response assessment in neuro-oncology relies on radiographic assessment of tumor burden on magnetic resonance (MR) imaging. The most widely used criteria were developed by the Response Assessment in Neuro-Oncology (RANO) group. The RANO criteria rely on bidimensional (2D) measurements of tumor on MR images. The RANO criteria were originally developed to assess response in adult high-grade glioma. However, the heterogeneous appearance of pediatric low-grade gliomas make application of RANO criteria challenging. Volumetric assessment of pediatric gliomas may offer a more comprehensive method for characterizing response. The goal of this thesis was to compare 2D and volumetric assessment methods in two pediatric glioma clinical trials from the Pacific Pediatric Neuro-Oncology Consortium (PNOC). The primary purpose of the thesis was to compare 2D and volumetric response to a clinical reference standard – neuroradiologist visual response assessment via the Brain Tumor Reporting and Data System (BT-RADS). A secondary aim was to determine optimal thresholds for categorizing volumetric response using BT-RADS as a reference standard. A third aim was to compare 2D and volumetric posttreatment trajectories in trial participants. Retrospective analyses of two pediatric glioma clinical trials (PNOC-001 and PNOC-002) were conducted. Changes in tumor 2D area, whole tumor volume, and solid tumor volume were compared to assess response. Follow-up images were assigned a response score on BT-RADS by two neuroradiologists. Empirical receiver operating characteristic (ROC) curves of changes in 2D area, whole, and solid tumor volume were constructed to classify partial response (PR) and progressive disease (PD) based on BT-RADS. In the PNOC-002 trial, a mathematical model was used to construct posttreatment trajectories of changes in 2D area and whole tumor volume in a subset of participants. Empirical ROC curves to classify BT-RADS PD among the 65 follow-up images assessed in the PNOC-001 trial yielded an AUC of 0.78 (95% CI: 0.66-0.90) for 2D area percent change, 0.84 (95% CI: 0.74-0.94) for whole volume percent change, and 0.96 (95% CI: 0.92-1.00) for solid volume percent change. DeLong tests revealed that there was a significant increase in AUC of the solid volume ROC curve compared to both 2D area (p = 0.005) and whole volume (p = 0.006). The empirical ROC curves to classify BT-RADS PR yielded an AUC of 0.87 (95% CI: 0.77-0.96) for 2D area percent change, 0.84 (95% CI: 0.70-0.99) for whole volume percent change, and 0.97 (95% CI: 0.94-1.00) for solid volume percent change. DeLong tests revealed that there was a significant increase in AUC of the solid volume ROC curve compared to 2D area (p = 0.02) but not whole volume (p = 0.08). The thresholds for solid volume percent change that included an 80% sensitivity in their 95% confidence intervals for classifying BT-RADS PD ranged from 15-25% and 15-20% for classifying BT-RADS PR. The empirical ROC curves for classification of BT-RADS PR in the 31 participants at the end of treatment or last available follow-up produced the following AUC values: 0.92 (95% CI: 0.80-1.00) for 2D area percent change, 0.99 (95% CI: 0.97-1.00) for whole volume percent change, and 0.99 (95% CI: 0.97-1.00) for solid volume percent change. DeLong test revealed no statistically significant difference in AUC between 2D area and either solid (p = 0.17) or whole volume (p = 0.17) ROC curves. The empirical ROC curves for classification of BT-RADS PR at the first time of BT-RADS PR detection produced the following AUC values: 0.84 (95% CI: 0.69-0.99) for 2D area percent change, 0.91 (95% CI: 0.80-1.00) for whole volume percent change, and 0.92 (95% CI: 0.82-1.00) for solid volume percent change. There was no statistically significant difference in AUC between the 2D area ROC curve and either solid (p = .34) or whole volume (p = .39) ROC curves based on DeLong tests. Based on mathematically modeled trajectories, there was no significant correlation in time to best response obtained from 2D area vs. whole volume posttreatment changes (? = 0.39, p = 0.054). Eight out of 25 participants (32%) had a difference of 90 days or more in transition time from partial response to stable disease between 2D area and whole volume trajectories. Moreover, of the 16 participants with tumor regrowth following stable disease, 50% had a difference of 90 days or more in transition time from stable disease to progressive disease between 2D area and whole volume trajectories. Solid tumor volume better predicted neuroradiologist assessment of partial response and progressive disease according to BT-RADS criteria in the PNOC-001 trial but performed as well as 2D measurements in classifying partial response in the PNOC-002 trial. Although volumetrics was not consistently superior to 2D measurements in detecting response in our study, there were differences in individual participant 2D and volumetric posttreatment trajectories. Future research comparing volumetric to 2D assessment in prospective trials is required to understand the significance of these differences to clinical management

Individualised Clinical Neuroimaging in the Developing Brain: Abnormality Detection

Perinatal neuroanatomical structure is incredibly intricate and, at time of birth, is undergoing continuous change due to interweaving developmental processes (growth, myelination and gyrification). While there is some small variability in structure and rates of development, all follow proscribed pathways with well documented milestones. Brain injury or other disruption of these processes can result in poor neurodevelopmental outcomes or mortality, making their early identification critical to estimate, and potentially forestall, negative effects. MRI is an increasingly used method of investigating suspected neonatal encephalopathies and injuries.Identification of these injuries and malformations is more challenging in neonates compared to adults due to the brain’s continuously evolving appearance. This makes radiological review of neonatal MRI an intensive and time-consuming task which, in an ideal setting, requires a team of highly skilled clinicians and radiologists with complementary training and extensive experience. To assist this review process, some localisation method that highlights areas likely to contain tissue abnormalities would be highly desirable, as it could quickly draw attention to these locations. In addition, identifying neonates whose MRI is likely to contain some form of pathology could allow for review prioritisation.In this thesis, I first investigated using normative models of neonatal tissue intensity for brain tissue abnormality detection. I applied voxel-wise Gaussian process (GP) regression to a training cohort of neonates with no obvious lesions, all born preterm (&lt;37 weeks) but imaged between 28-55 weeks. Gestational age at birth (GA), postmenstrual age at scan (PMA) and sex were used as input variables and voxel intensity as the output variable. GPs output a mean value and its variance inferred from neonates within the training cohort whose demographic information most closely matched those of the prediction target. The voxel specific models were put together to form a synthesised typical image and standard deviation image derived from the variance outputs. Z-score abnormality maps were constructed by taking the difference between neonates actual MRI and GP-calculated synthetic image and scaling by their standard deviation map. Higher Z-score map values indicate voxels more likely to contain abnormal tissue intensity. Using manually delineated masks of common brain injuries seen in a subset of neonates, these abnormality Z-score maps demonstrated good detection performance using area under the curve of receiver operating characteristic scores, with the exception of small punctate lesions.The initial voxel-wise models had substantial false positives around the edges of the brain where there is large typical heterogeneity. I next investigated if incorporating local structural information into predictive models could improve their ability to accommodate typical anatomical heterogeneity seen across individual brains and improve the accuracy of synthetic images and abnormality detection. To achieve this, voxel intensity values in a patch surrounding the prediction target were appended to the design matrix, alongside GMA, PMA and sex. The patch-based synthetic images were able to match an individual’s brain structure more closely and had lower false positives in normal appearing tissue. However, a weakness was that the centre of some larger lesions was included in the predictions (thereby classified as ‘healthy’ tissue), having a deleterious effect on their coverage, increasing false negatives. This was offset by much better coverage of smaller, more subtle lesions, to the extent that overall performance was higher compared to that seen in the earlier model.I also investigated if the Z-score abnormality maps could be used to classify neonates with MRI positive brain injury from those with normal appearing brains. While many machine learning algorism see frequent use in neuroimaging classification tasks, I opted for a logistic regression model due to its high levels of interpretability and simple implementation. Using the histograms of the Z-score abnormality maps as inputs, the model demonstrated good performance, being able to correctly identify neonates with injuries, but not those with subtle lesions like punctate lesions, whilst minimising false identification of neonates with normal appearing brains.To ascertain if performance could be improved, I explored multiple classification methods. Specifically, the use of other more complex classifiers (random forest, support vector machines, GP classification) and the use of a regional abnormal voxel count, that allowed localisation of lesioned tissue rather than the more global detection ability of the Z-score histograms. Using these innovations, I investigated their application towards a specific pathology; hypoxic ischemic encephalopathy (HIE). This is a good test for the system, as HIE has high incidence rates, multiple associated lesion types and a time dependant appearance. Further, I wanted to know if, given a positive HIE diagnosis, the Z-score abnormality maps could be used to predict long-term outcomes (normal vs poor). Several models demonstrated an excellent ability to separate HIE and healthy control neonates achieving &gt;90% accuracy, a statistically significant result even after false discovery rate (FDR) correction (p-value &lt; 0.05). While the outcome prediction models achieved reasonable accuracy, &gt;70% in multiple models, none of these were statistically significant after FDR correction.Overall, this work demonstrates how normative modelling can be used to create individual voxel-wise / image-wise estimation of tissue abnormality for neonatal MRI across a range of gestational ages. It further demonstrates that these abnormality maps can be utilised for additional tasks, in this instance, three increasingly challenging neurological classification problems. These include the separation of neonates with and without MRI positive lesions, identification of neonates with a specific pathological condition (HIE) and prediction of long-term functional outcome (normal vs poor). Within a radiological setting, these classifications task can be considered analogous to three radiological challenges, image triage, diagnostic detection and estimation of developmental prognosis, important for the clinical team but also infants and their families

Variable Scale Statistics For Cardiac Segmentation and Shape Analysis

A novel framework for medical image analysis, known as Shells and Spheres, has been developed by our research lab. This framework utilizes spherical operators of variable radius, centered at each image pixel and sized to reach, but not cross, the nearest boundary. Statistical population tests are performed on the populations of pixels within adjacent spheres to compare image regions across boundaries, delineating bothindependent image objects and the boundaries between them. This research has focused on developing the Shells and Spheres frameworkand applying it to the problem of segmentation of anatomical objects. Furthermore, we have rigorously studied the framework and its applications to clinical segmentation, validating and improving our n-dimensional segmentation algorithm. To this end, we have enhanced the original Shells and Spheres segmentation algorithm by adding a priori information, developing techniques for optimizing algorithm parameters, implementing a software platform for experimentation, and performing validation experiments using real 3D ovine cardiac MRI data. The system developed provides automated 3D segmentation given a priori information in the form of a trivial 2D manual training procedure, which involves tracing a single 2D contour from which 3D algorithm parameters are then automatically derived. We apply this system tosegmentation of the Right Ventricular Outflow Tract (RVOT) to aid in research toward the creation of a Tissue Engineered Pulmonary Valve(TEPV). Experimental methods are presented for the development and validation of the system, as well as a detailed description of the Shells and Spheres framework, our segmentation algorithm, and the clinical significance of this work

Translation of quantitative MRI analysis tools for clinical neuroradiology application

Quantification of imaging features can assist radiologists by reducing subjectivity, aiding detection of subtle pathology, and increasing reporting consistency. Translation of quantitative image analysis techniques to clinical use is currently uncommon and challenging. This thesis explores translation of quantitative imaging support tools for clinical neuroradiology use. I have proposed a translational framework for development of quantitative imaging tools, using dementia as an exemplar application. This framework emphasises the importance of clinical validation, which is not currently prioritised. Aspects of the framework were then applied to four disease areas: hippocampal sclerosis (HS) as a cause of epilepsy; dementia; multiple sclerosis (MS) and gliomas. A clinical validation study for an HS quantitative report showed that when image interpreters used the report, they were more accurate and confident in their assessments, particularly for challenging bilateral cases. A similar clinical validation study for a dementia reporting tool found improved sensitivity for all image interpreters and increased assessment accuracy for consultant radiologists. These studies indicated benefits from quantitative reports that contextualise a patient’s results with appropriate normative reference data. For MS, I addressed a technical translational challenge by applying lesion and brain quantification tools to standard clinical image acquisitions which do not include a conventional T1-weighted sequence. Results were consistent with those from conventional sequence inputs and therefore I pursued this concept to establish a clinically applicable normative reference dataset for development of a quantitative reporting tool for clinical use. I focused on current radiology reporting of gliomas to establish which features are commonly missed and may be important for clinical management decisions. This informs both the potential utility of a quantitative report for gliomas and its design and content. I have identified numerous translational challenges for quantitative reporting and explored aspects of how to address these for several applications across clinical neuroradiology

Large-scale medical image annotation with quality-controlled crowdsourcing

Accurate annotations of medical images are essential for various clinical applications. The remarkable advances in machine learning, especially deep learning based techniques, show great potential for automatic image segmentation. However, these solutions require a huge amount of accurately annotated reference data for training. Especially in the domain of medical image analysis, the availability of domain experts for reference data generation is becoming a major bottleneck for machine learning applications. In this context, crowdsourcing has gained increasing attention as a tool for low-cost and large-scale data annotation. As a method to outsource cognitive tasks to anonymous non-expert workers over the internet, it has evolved into a valuable tool for data annotation in various research fields. Major challenges in crowdsourcing remain the high variance in the annotation quality as well as the lack of domain specific knowledge of the individual workers. Current state-of-the-art methods for quality control usually induce further costs, as they rely on a redundant distribution of tasks or perform additional annotations on tasks with already known reference outcome. Aim of this thesis is to apply common crowdsourcing techniques for large-scale medical image annotation and create a cost effective quality control method for crowd-sourced image annotation. The problem of large-scale medical image annotation is addressed by introducing a hybrid crowd-algorithm approach that allowed expert-level organ segmentation in CT scans. A pilot study performed on the case of liver segmentation in abdominal CT scans showed that the proposed approach is able to create organ segmentations matching the quality of those create by medical experts. Recording the behavior of individual non-expert online workers during the annotation process in clickstreams enabled the derivation of an annotation quality measure that could successfully be used to merge crowd-sourced segmentations. A comprehensive validation study performed with various object classes from publicly available data sets demonstrated that the presented quality control measure generalizes well over different object classes and clearly outperforms state-of-the-art methods in terms of costs and segmentation quality. In conclusion, the methods introduced in this thesis are an essential contribution to reduce the annotation costs and further improve the quality of crowd-sourced image segmentation

Three-dimensional anatomical atlas of the human body

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsAnatomical atlases allow mapping the anatomical structures of the human body. Early versions of these systems consisted of analogic representations with informative text and labelled images of the human body. With the advent of computer systems, digital versions emerged and the third dimension was introduced. Consequently, these systems increased their efficiency, allowing more realistic visualizations with improved interactivity. The development of anatomical atlases in geographic information systems (GIS) environments allows the development of platforms with a high degree of interactivity and with tools to explore and analyze the human body. In this thesis, a prototype for the human body representation is developed. The system includes a 3D GIS topological model, a graphical user interface and functions to explore and analyze the interior and the surface of the anatomical structures of the human body. The GIS approach relies essentially on the topological characteristics of the model and on the kind of available functions, which include measurement, identification, selection and analysis. With the incorporation of these functions, the final system has the ability to replicate the kind of information provided by the conventional anatomical atlases and also provides a higher level of functionality, since some of the atlases limitations are precisely features offered by GIS, namely, interactive capabilities, multilayer management, measurement tools, edition mode, allowing the expansion of the information contained in the system, and spatial analyzes

In vivo analyses of the correlates of cortical and white matter pathology in patients with multiple sclerosis by quantitative 7 Tesla and 3 Tesla MRI and molecular imaging.

This thesis is divided in two sections. The first reports the results from a project regarding the application of ultra-high-field quantitative MRI for the study of cortical grey matter pathology in patients with early multiple sclerosis (MS). Applying a Combined Myelin Estimation method, obtained by 7 Tesla magnetic resonance imaging, we aimed at characterizing cortical microstructural abnormalities related to myelin content in cortical lesions and normal-appearing cortex, to assess their evolution at 1-year follow-up and to relate cortical myelin changes to clinical and radiological disease burden. Data obtained from 25 patients with early MS and 19 healthy volunteers showed overall abnormally low myelin content in cortical lesions and several areas of normal-appearing cortex. Myelin content along the cortex correlated with neurological impairment. Individual cortical lesion analysis revealed heterogenous patterns, ranging from extensive to partial demyelination, or even measurements comparable to the healthy group. At 1-year follow-up, cortical myelin was overall decreased in cortical lesions and scattered areas in the normal-appearing cortex. Diffusion metrics obtained in the same regions did not show the presence of cortical neural loss accompanying early demyelination. The second section reports results from a study in which we combined 11C-PBR28 positron-emission tomography, marking activated microglia, with the more recently validated synthetic magnetic resonance imaging for myelin content assessment, aiming at researching the presence of correlates of pathology in the white matter of patients with multiple sclerosis at different disease stages, and assessing the interplay between neuroinflammation and demyelination. We found abnormal increase of microglia activation in MS patients compared to healthy volunteers in several areas across the white matter, correlating with clinical and radiological disease burden. An individual analysis of white matter lesions showed the presence of active lesions in the early phases of the disease, evolving in inactive or peripherally active lesions in the late disease phases, the latter correlating with clinical disability. Myelin content in the normal-appearing white matter of MS patients correlated with neurological impairment and lesion load. Higher microglia activation in the white matter lesions was related to lower myelin content in the non-lesioned white matter

Patch-based segmentation with spatial context for medical image analysis

Accurate segmentations in medical imaging form a crucial role in many applications from pa- tient diagnosis to population studies. As the amount of data generated from medical images increases, the ability to perform this task without human intervention becomes ever more de- sirable. One approach, known broadly as atlas-based segmentation, is to propagate labels from images which have already been manually labelled by clinical experts. Methods using this ap- proach have been shown to be e ective in many applications, demonstrating great potential for automatic labelling of large datasets. However, these methods usually require the use of image registration and are dependent on the outcome of the registration. Any registrations errors that occur are also propagated to the segmentation process and are likely to have an adverse e ect on segmentation accuracy. Recently, patch-based methods have been shown to allow a relaxation of the required image alignment, whilst achieving similar results. In general, these methods label each voxel of a target image by comparing the image patch centred on the voxel with neighbouring patches from an atlas library and assigning the most likely label according to the closest matches. The main contributions of this thesis focuses around this approach in providing accurate segmentation results whilst minimising the dependency on registration quality. In particular, this thesis proposes a novel kNN patch-based segmentation framework, which utilises both intensity and spatial information, and explore the use of spatial context in a diverse range of applications. The proposed methods extend the potential for patch-based segmentation to tolerate registration errors by rede ning the \locality" for patch selection and comparison, whilst also allowing similar looking patches from di erent anatomical structures to be di erentiated. The methods are evaluated on a wide variety of image datasets, ranging from the brain to the knees, demonstrating its potential with results which are competitive to state-of-the-art techniques.Open Acces