Topo-Geometric Filtration Scheme for Geometric Active Contours and Level Sets: Application to Cerebrovascular Segmentation

One of the main problems of the existing methods for the segmentation of cerebral vasculature is the appearance in the segmentation result of wrong topological artefacts such as the kissing vessels. In this paper, a new approach for the detection and correction of such errors is presented. The proposed technique combines robust topological information given by Persistent Homology with complementary geometrical information of the vascular tree. The method was evaluated on 20 images depicting cerebral arteries. Detection and correction success rates were 81.80% and 68.77%, respectively

Explainable Disease Classification via weakly-supervised segmentation

Deep learning based approaches to Computer Aided Diagnosis (CAD) typically pose the problem as an image classification (Normal or Abnormal) problem. These systems achieve high to very high accuracy in specific disease detection for which they are trained but lack in terms of an explanation for the provided decision/classification result. The activation maps which correspond to decisions do not correlate well with regions of interest for specific diseases. This paper examines this problem and proposes an approach which mimics the clinical practice of looking for an evidence prior to diagnosis. A CAD model is learnt using a mixed set of information: class labels for the entire training set of images plus a rough localisation of suspect regions as an extra input for a smaller subset of training images for guiding the learning. The proposed approach is illustrated with detection of diabetic macular edema (DME) from OCT slices. Results of testing on on a large public dataset show that with just a third of images with roughly segmented fluid filled regions, the classification accuracy is on par with state of the art methods while providing a good explanation in the form of anatomically accurate heatmap /region of interest. The proposed solution is then adapted to Breast Cancer detection from mammographic images. Good evaluation results on public datasets underscores the generalisability of the proposed solution

Robustness of common hemodynamic indicators with respect to numerical resolution in 38 middle cerebral artery aneurysms

Background: Using computational fluid dynamics (CFD) to compute the hemodynamics in cerebral aneurysms has received much attention in the last decade. The usability of these methods depends on the quality of the computations, highlighted in recent discussions. The purpose of this study is to investigate the convergence of common hemodynamic indicators with respect to numerical resolution. Methods: 38 middle cerebral artery bifurcation aneurysms were studied at two different resolutions (one comparable to most studies, and one finer). Relevant hemodynamic indicators were collected from two of the most cited studies, and were compared at the two refinements. In addition, correlation to rupture was investigated. Results: Most of the hemodynamic indicators were very well resolved at the coarser resolutions, correlating with the finest resolution with a correlation coefficient >0.95. The oscillatory shear index (OSI) had the lowest correlation coefficient of 0.83. A logarithmic Bland-Altman plot revealed noticeable variations in the proportion of the aneurysm under low shear, as well as in spatial and temporal gradients not captured by the correlation alone. Conclusion: Statistically, hemodynamic indicators agree well across the different resolutions studied here. However, there are clear outliers visible in several of the hemodynamic indicators, which suggests that special care should be taken when considering individual assessment

Why rankings of biomedical image analysis competitions should be interpreted with care

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future

Current Wildland Fire Patterns and Challenges in Europe: A Synthesis of National Perspectives

Changes in climate, land use, and land management impact the occurrence and severity of wildland fires in many parts of the world. This is particularly evident in Europe, where ongoing changes in land use have strongly modified fire patterns over the last decades. Although satellite data by the European Forest Fire Information System provide large-scale wildland fire statistics across European countries, there is still a crucial need to collect and summarize in-depth local analysis and understanding of the wildland fire condition and associated challenges across Europe. This article aims to provide a general overview of the current wildland fire patterns and challenges as perceived by national representatives, supplemented by national fire statistics (2009–2018) across Europe. For each of the 31 countries included, we present a perspective authored by scientists or practitioners from each respective country, representing a wide range of disciplines and cultural backgrounds. The authors were selected from members of the COST Action “Fire and the Earth System: Science & Society” funded by the European Commission with the aim to share knowledge and improve communication about wildland fire. Where relevant, a brief overview of key studies, particular wildland fire challenges a country is facing, and an overview of notable recent fire events are also presented. Key perceived challenges included (1) the lack of consistent and detailed records for wildland fire events, within and across countries, (2) an increase in wildland fires that pose a risk to properties and human life due to high population densities and sprawl into forested regions, and (3) the view that, irrespective of changes in management, climate change is likely to increase the frequency and impact of wildland fires in the coming decades. Addressing challenge (1) will not only be valuable in advancing national and pan-European wildland fire management strategies, but also in evaluating perceptions (2) and (3) against more robust quantitative evidence

Automatic labeling of vascular structures with topological constraints via HMM [Research data]

##Compile environment Windows 7(64-bit) Intel(R) Core(TM) i7-4790 CPU @ 3.6GHz RAM: 8.00GB Microsoft Visual Studio 2012 Python 2.7 Anaconda 4.1.0 (64-bit) XGBoost Library 0.4 (https://github.com/dmlc/xgboost/tree/master/windows) Scikit-Learn Library 0.18.1 hmmlearn 0.2.0 NURBS open-source library ## Running the code This file contains a summary of what you will find in each of the files that make up our experiments.. Step0: PreprocessingData Our proposed approach has been evaluated on the public dataset distributed by the MIDAS Data Server at Kitware Inc.. It contains 50 MRA images of the cerebral vasculature from healthy volunteers together with theirs segmentations and centerlines. (Bogunović et al. "Anatomical Labeling of the Circle of Willis Using Maximum A Posteriori Probability Estimation." IEEE Transactions on Medical Imaging 32(9) (2013):1587) We first prune the centerline model to a region around the CoW. “FeatureGenerating/data/skeleton”. Step1: FeatureGenerating(C/C++): To generate a feature matrix “FeatureGenerating/feature” from the skeleton data “FeatureGenerating/data/skeleton” that has been marked with Ground Truth“FeatureGenerating/data/cood.txt”. We employed the NURBS curve with features calculate available in NURBS open-source library. To compile the code, you also need to include the library. Step2: Pre_ML (C/C++): To Separate feature matrix “FeatureGenerating/feature” into the training set “data/ML/XXX/train.txt” and corresponding test set “data/ML/XXX/test.txt”. Step3: XGBoost(Python): To train model based on the training set in “data/ML”, and predict the results ”data/res_XGBoost” of corresponding test set. To compile the code, you also need to include the XGBoost library. Step4: Chain(C/C++): To “sort” the bifurcation and construct observation sequences “data/obs_list” and status sequences “data/GT_list” based on the results of XGBoost. Step5: Pre_HMM(C/C++): To generates 50 sets of observation matrices “data/obs” and transfer matrices “data/trans” based on observation sequences and state sequences. Row 1 in “seg/XXX” is the sequence of state, and Row 2 in “data/seg/XXX” is its corresponding sequence of observations. Step6: HMM(Python): Hidden Markov Process. Input:”data/seg/XXX”, “data/obs”, “data/trans” In the file “data/res_topo”, Row 1 is the results, and Row 2 is its corresponding Ground Truth. To compile the code, you also need to include the hmmlearn library. Step7: Result analysis: Metrics. In the file”data/matrix_XGBoost”and ”data/matrix_topo”, the first part is TP, FN, FP, TN value, the second part is A, P, R, S value, the last part is the confusion matrix.The project contains the implementation of the method described in: Wang et al., "Automatic labeling of vascular structures with topological constraints via HMM", MICCAI 2017. We propose a novel graph labeling approach to anatomically label vascular structures of interest. Our algorithm can handle different topologies, like circle, chain and tree. By using coordinate independent geometrical features, it does not require prior global alignment.This research was partially supported by the Chinese High-Technical Research Development Foundation (863) Program (No.2015AA020506), Beijing Natural Science Foundation of China(No.4172033), the Spanish Ministry of Economy and Competitiveness, through the Maria de Maeztu Programme for Centres/Units of Excellence in R&D (MDM-2015-0502), and the Spanish Ministry of Economy and Competitiveness (DEFENSE project, TIN2013-47913-C3-1-R)

Automated deep learning-based AMD detection and staging in real-world OCT datasets (PINNACLE study report 5)

Real-world retinal optical coherence tomography (OCT) scans are available in abundance in primary and secondary eye care centres. They contain a wealth of information to be analyzed in retrospective studies. The associated electronic health records alone are often not enough to generate a high-quality dataset for clinical, statistical, and machine learning analysis. We have developed a deep learning-based age-related macular degeneration (AMD) stage classifier, to efficiently identify the first onset of early/intermediate (iAMD), atrophic (GA), and neovascular (nAMD) stage of AMD in retrospective data. We trained a two-stage convolutional neural network to classify macula-centered 3D volumes from Topcon OCT images into 4 classes: Normal, iAMD, GA and nAMD. In the first stage, a 2D ResNet50 is trained to identify the disease categories on the individual OCT B-scans while in the second stage, four smaller models (ResNets) use the concatenated B-scan-wise output from the first stage to classify the entire OCT volume. Classification uncertainty estimates are generated with Monte-Carlo dropout at inference time. The model was trained on a real-world OCT dataset, 3765 scans of 1849 eyes, and extensively evaluated, where it reached an average ROC-AUC of 0.94 in a real-world test set