Atlas-Based Segmentation of Brain Tumor Images Using a Markov Random Field-Based Tumor Growth Model and Non-Rigid Registration

We propose a new and clinically oriented approach to perform atlas-based segmentation of brain tumor images. A mesh-free method is used to model tumor-induced soft tissue deformations in a healthy brain atlas image with subsequent registration of the modified atlas to a pathologic patient image. The atlas is seeded with a tumor position prior and tumor growth simulating the tumor mass effect is performed with the aim of improving the registration accuracy in case of patients with space-occupying lesions. We perform tests on 2D axial slices of five different patient data sets and show that the approach gives good results for the segmentation of white matter, grey matter, cerebrospinal fluid and the tumor.

Automated cement segmentation in vertebroplasty

Vertebroplasty is a minimally invasive procedure with many benefits; however, the procedure is not without risks and potential complications, of which leakage of the cement out of the vertebral body and into the surrounding tissues is one of the most serious. Cement can leak into the spinal canal, venous system, soft tissues, lungs and intradiscal space, causing serious neurological complications, tissue necrosis or pulmonary embolism. We present a method for automatic segmentation and tracking of bone cement during vertebroplasty procedures, as a first step towards developing a warning system to avoid cement leakage outside the vertebral body. We show that by using active contours based on level sets the shape of the injected cement can be accurately detected. The model has been improved for segmentation as proposed in our previous work by including a term that restricts the level set function to the vertebral body. The method has been applied to a set of real intra-operative X-ray images and the results show that the algorithm can successfully detect different shapes with blurred and not well-defined boundaries, where the classical active contours segmentation is not applicable. The method has been positively evaluated by physicians.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model

We present an automatic method to segment brain tissues from volumetric MRI brain tumor images. The method is based on non-rigid registration of an average atlas in combination with a biomechanically justified tumor growth model to simulate soft-tissue deformations caused by the tumor mass-effect. The tumor growth model, which is formulated as a mesh-free Markov Random Field energy minimization problem, ensures correspondence between the atlas and the patient image, prior to the registration step. The method is non-parametric, simple and fast compared to other approaches while maintaining similar accuracy. It has been evaluated qualitatively and quantitatively with promising results on eight datasets comprising simulated images and real patient data.

Long-term effects of watershed management on surface runoff and sediment loss in the Ethiopian Highlands

Data on rainfall, runoff and sediment loss from different land use types have been collected by the Soil Conservation Research Programme in seven small catchments (73-673 hectares) throughout the Ethiopian Highlands since the early 1980s. Monitoring was carried out on a storm-to-storm basis for extended periods of 10-20 years, and the data are analysed here to assess long-term effects of changes. Soil and water conservation technologies were introduced in the early years in the catchments in view of their capacity to reduce runoff and sediment yield. Results indicate that rainfall did not substantially change over the observation periods. Land use changes and land degradation, however, altered runoff, as shown by the data from small test plots (30 m2), which were not altered by conservation measures during the monitoring periods. Sediment delivery from the catchments may have decreased due to soil and water conservation, while runoff rates did not change significantly. Extrapolation of the results in the highlands, however, showed that expansion of cultivated and grazing land induced by population growth may have increased the overall surface runoff. Watershed management in the catchments, finally, had beneficial effects on ecosystem services by reducing soil erosion, restoring soil fertility, enhancing agricultural production, and maintaining overall runoff to the benefit of lowland areas and neighbouring countries.

Mainstreaming a Decade's Experiences in Watershed Management to Meet the Challenges of Environmental Change in the Hindu Kush-Himalayas

Like other mountain areas in the world, the Hindu Kush-Himalayan (HKH) region is particularly vulnerable to climate change. Ongoing climate change processes are projected to have a high impact on the HKH region, and accelerated warming has been reported in the Himalayas. These climate change impacts will be superimposed on a variety of other environmental and social stresses, adding to the complexity of the issues. The sustainable use of natural resources is crucial to the long-term stability of the fragile mountain ecosystems in the HKH and to sustain the socio-ecological resilience that forms the basis of sustainable livelihoods in the region. In order to be prepared for these challenges, it is important to take stock of previous research. The ‘People and Resource Dynamics Project’ (PARDYP), implemented by International Centre for Integrated Mountain Development (ICIMOD), provides a variety of participatory options for sustainable land management in the HKH region. The PARDYD project was a research for development project that operated in five middle mountain watersheds across the HKH – two in Nepal and one each in China, India, and Pakistan. The project ran from 1996 to 2006 and focused on addressing the marginalisation of mountain farmers, the use and availability of water, issues relating to land and forest degradation and declining soil fertility, the speed of regeneration of degraded land, and the ability of the natural environment to support the growing needs of the region’s increasing population. A key learning from the project was that the opinion of land users is crucial to the acceptance (and, therefore, successful application) of new technologies and approaches. A major challenge at the end of every project is to promote knowledge sharing and encourage the cross-fertilization of ideas (e.g., in the case of PARDYP, with other middle mountain inhabitants and practitioners in the region) and to share lessons learned with a wider audience. This paper will highlight how the PARDYP findings, including ways of addressing soil fertility and water scarcity, have been mainstreamed in the HKH region through capacity building (international, regional, and national training courses), networking, and the provision of backstopping services. In addition, in view of the challenges in watershed management in the HKH connected to environmental change, the lessons learned from the PARDYP are now being used by ICMOD to define and package climate change proof technology options to address climate change adaptation.

Pathology hinting as the combination of automatic segmentation with a statistical shape model

With improvements in acquisition speed and quality, the amount of medical image data to be screened by clinicians is starting to become challenging in the daily clinical practice. To quickly visualize and find abnormalities in medical images, we propose a new method combining segmentation algorithms with statistical shape models. A statistical shape model built from a healthy population will have a close fit in healthy regions. The model will however not fit to morphological abnormalities often present in the areas of pathologies. Using the residual fitting error of the statistical shape model, pathologies can be visualized very quickly. This idea is applied to finding drusen in the retinal pigment epithelium (RPE) of optical coherence tomography (OCT) volumes. A segmentation technique able to accurately segment drusen in patients with age-related macular degeneration (AMD) is applied. The segmentation is then analyzed with a statistical shape model to visualize potentially pathological areas. An extensive evaluation is performed to validate the segmentation algorithm, as well as the quality and sensitivity of the hinting system. Most of the drusen with a height of 85.5 microm were detected, and all drusen at least 93.6 microm high were detected.

Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ±0.54 μm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.

Distance measures for image segmentation evaluation

The task considered in this paper is performance evaluation of region segmentation algorithms in the ground-truth-based paradigm. Given a machine segmentation and a ground-truth segmentation, performance measures are needed. We propose to consider the image segmentation problem as one of data clustering and, as a consequence, to use measures for comparing clusterings developed in statistics and machine learning. By doing so, we obtain a variety of performance measures which have not been used before in image processing. In particular, some of these measures have the highly desired property of being a metric. Experimental results are reported on both synthetic and real data to validate the measures and compare them with others.

Early assessment of brain maturation by MR imaging segmentation in neonates and premature infants

PURPOSE: We evaluated the impact of premature extrauterine life on brain maturation. PATIENTS AND METHODS: Twelve neonates underwent MR imaging at 40 (39.64 +/- 0.98) weeks (full term). Fifteen premature infants underwent 2 MR imaging examinations, after birth (preterm at birth) and at 40 weeks (41.03 +/- 1.33) (preterm at term). A 3D MR imaging technique was used to measure brain volumes compared with intracranial volume: total brain volume, cortical gray matter, myelinated white matter, unmyelinated white matter, basal ganglia (BG), and CSF. RESULTS: The average absolute volume of intracranial volume (269.8 mL +/- 36.5), total brain volume (246.5 +/- 32.3), cortical gray matter (85.53 mL +/- 22.23), unmyelinated white matter (142.4 mL +/-14.98), and myelinated white matter (6.099 mL +/-1.82) for preterm at birth was significantly lower compared with that for the preterm at term: the average global volume of intracranial volume (431.7 +/- 69.98), total brain volume (391 +/- 66,1), cortical gray matter (179 mL +/- 41.54), unmyelinated white matter (185.3 mL +/- 30.8), and myelinated white matter (10.66 mL +/- 3.05). It was also lower compared with that of full-term infants: intracranial volume (427.4 mL +/- 53.84), total brain volume (394 +/- 49.22), cortical gray matter (181.4 +/- 29.27), unmyelinated white matter (183.4 +/- 27.37), and myelinated white matter (10.72 +/- 4.63). The relative volume of cortical gray matter (30.62 +/- 5.13) and of unmyelinated white matter (53.15 +/- 4.8) for preterm at birth was significantly different compared with the relative volume of cortical gray matter (41.05 +/- 5.44) and of unmyelinated white matter (43.22 +/- 5.11) for the preterm at term. Premature infants had similar brain tissue volumes at 40 weeks to full-term infants. CONCLUSION: MR segmentation techniques demonstrate that cortical neonatal maturation in moderately premature infants at term and term-born infants was similar.

GIS based Watershed Classification in Lao PDR

Every inclined land surface has a potential for soil and water degradation, the seriousness depends on a multitude of parameters such as slope, soil type, geomorphology, rainfall, land use and natural vegetation cover. In Laos this intensified land use leads to reduced vegetation cover, to increased soil erosion, decreasing yield, and finally is likely to influence the hydrological regime. Against this background the Mekong River Commission (MRC) elaborated a spatial explicit Watershed Classification (WSC) for the Lower Mekong Basin. Based on topographic factors derived from a high-resolution Digital Terrain Model, five watershed classes are calculated, giving indication about the sensitivity to resource degradation by soil erosion. The WSC allows spatial priority setting for watershed management and generally supports informed decision making on reconnaissance level. In the conclusions focus is laid on general considerations when GIS techniques are used for spatial decision support in a development context.