Open-box spectral clustering: applications to medical image analysis

Improving spike sorting efficiency with separability index and spectral clustering

This study explores the effectiveness of spectral clustering for spike sorting and proposes a Separability Index to measure the difficulty of spike sorting for a signal. The accuracy of spectral clustering is evaluated using different feature sets, including raw samples, first and second derivatives, and principal components analysis (PCA), and compared to two previously published methods. The results obtained over a dataset including 16 signals show that raw samples, with an average accuracy of 73.84 %, are effective for spectral clustering-based spike sorting. The analysis demonstrates that the proposed Separability Index can be utilized to classify signals beforehand, reducing the cost and processing time of large datasets. Furthermore, the proposed index can reveal spike sorting difficulty, making it a valuable tool for comparing the performance of various spike sorting methods in depth. The proposed method has higher accuracy (up to 23 %) compared to two previously published methods, and its accuracy is aligned with the Separability Index (correlation coefficient = 0.71). Overall, this study contributes to the field of spike sorting and offers insights into leveraging spectral clustering for this task.

Multi-View Discrete Clustering: A Concise Model

In most existing graph-based multi-view clustering methods, the eigen-decomposition of the graph Laplacian matrix followed by a post-processing step is a standard configuration to obtain the target discrete cluster indicator matrix. However, we can naturally realize that the results obtained by the two-stage process will deviate from that obtained by directly solving the primal clustering problem. In addition, it is essential to properly integrate the information from different views for the enhancement of the performance of multi-view clustering. To this end, we propose a concise model referred to as Multi-view Discrete Clustering (MDC), aiming at directly solving the primal problem of multi-view graph clustering. We automatically weigh the view-specific similarity matrix, and the discrete indicator matrix is directly obtained by performing clustering on the aggregated similarity matrix without any post-processing to best serve graph clustering. More importantly, our model does not introduce an additive, nor does it has any hyper-parameters to be tuned. An efficient optimization algorithm is designed to solve the resultant objective problem. Extensive experimental results on both synthetic and real benchmark datasets verify the superiority of the proposed model.

A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering

An important part of diagnostics is to gain insight into properties that characterize a disease. Machine learning has been used for this purpose, for instance, to identify biomarkers in genomics. However, when patient data are presented as images, identifying properties that characterize a disease becomes far more challenging. A common strategy involves extracting features from the images and analyzing their occurrence in healthy versus pathological images. A limitation of this approach is that the ability to gain new insights into the disease from the data is constrained by the information in the extracted features. Typically, these features are manually extracted by humans, which further limits the potential for new insights. To overcome these limitations, in this paper, we propose a novel framework that provides insights into diseases without relying on handcrafted features or human intervention. Our framework is based on deep learning (DL), explainable artificial intelligence (XAI), and clustering. DL is employed to learn deep patterns, enabling efficient differentiation between healthy and pathological images. Explainable artificial intelligence (XAI) visualizes these patterns, and a novel "explanation-weighted" clustering technique is introduced to gain an overview of these patterns across multiple patients. We applied the method to images from the gastrointestinal tract. In addition to real healthy images and real images of polyps, some of the images had synthetic shapes added to represent other types of pathologies than polyps. The results show that our proposed method was capable of organizing the images based on the reasons they were diagnosed as pathological, achieving high cluster quality and a rand index close to or equal to one.

A health data led approach for assessing potential health benefits of green and blue spaces: Lessons from an Irish case study

Research producing evidence-based information on the health benefits of green and blue spaces often has within its design, the potential for inherent or implicit bias which can unconsciously orient the outcomes of such studies towards preconceived hypothesis. Many studies are situated in proximity to specific or generic green and blue spaces (hence, constituting a green or blue space led approach), others are conducted due to availability of green and blue space data (hence, applying a green or blue space data led approach), while other studies are shaped by particular interests in the association of particular health conditions with presence of, or engagements with green or blue spaces (hence, adopting a health or health status led approach). In order to tackle this bias and develop a more objective research design for studying associations between human health outcomes and green and blue spaces, this paper discussed the features of a methodological framework suitable for that purpose after an initial, year-long, exploratory Irish study. The innovative approach explored by this study (i.e., the health-data led approach) first identifies sample sites with good and poor health outcomes from available health data (using data clustering techniques) before examining the potential role of the presence of, or engagement with green and blue spaces in creating such health outcomes. By doing so, we argue that some of the bias associated with the other three listed methods can be reduced and even eliminated. Finally, we infer that the principles and paradigm adopted by the health data led approach can be applicable and effective in analyzing other sustainability problems beyond associations between human health outcomes and green and blue spaces (e.g., health, energy, food, income, environment and climate inequality and justice etc.). The possibility of this is also discussed within this paper.

Visual Parameter Space Exploration in Time and Space

Computational models, such as simulations, are central to a wide range of fields in science and industry. Those models take input parameters and produce some output. To fully exploit their utility, relations between parameters and outputs must be understood. These include, for example, which parameter setting produces the best result (optimization) or which ranges of parameter settings produce a wide variety of results (sensitivity). Such tasks are often difficult to achieve for various reasons, for example, the size of the parameter space, and supported with visual analytics. In this paper, we survey visual parameter space exploration (VPSE) systems involving spatial and temporal data. We focus on interactive visualizations and user interfaces. Through thematic analysis of the surveyed papers, we identify common workflow steps and approaches to support them. We also identify topics for future work that will help enable VPSE on a greater variety of computational models.

Visualization and Visual Analytics Approaches for Image and Video Datasets: A Survey

Image and video data analysis has become an increasingly important research area with applications in different domains such as security surveillance, healthcare, augmented and virtual reality, video and image editing, activity analysis and recognition, synthetic content generation, distance education, telepresence, remote sensing, sports analytics, art, non-photorealistic rendering, search engines, and social media. Recent advances in Artificial Intelligence (AI) and particularly deep learning have sparked new research challenges and led to significant advancements, especially in image and video analysis. These advancements have also resulted in significant research and development in other areas such as visualization and visual analytics, and have created new opportunities for future lines of research. In this survey paper, we present the current state of the art at the intersection of visualization and visual analytics, and image and video data analysis. We categorize the visualization papers included in our survey based on different taxonomies used in visualization and visual analytics research. We review these papers in terms of task requirements, tools, datasets, and application areas. We also discuss insights based on our survey results, trends and patterns, the current focus of visualization research, and opportunities for future research.

A multilayer network-enabled ultrasonic image series analysis approach for online cancer drug delivery monitoring

The objective of this study is to develop an effective data-driven methodology for the online monitoring of cancer drug delivery guided by the ultrasonic images. To achieve this goal, effective image quantification and accurate feature extraction play a critical role on image-guided drug delivery (IGDD) monitoring. However, the existing image-guided approaches in such area are mainly focused on the analysis for individual images rather than the image series. In fact, the temporal patterns between consecutive images may contain critical information and it is necessary to be considered in the monitoring analysis. In addition, the conventional approaches, such as the pure intensity-based method, also do not sufficiently consider the effects of noise in the ultrasonic images, which also limits the monitoring sensitivity and accuracy. To address the challenges, this paper proposed a novel multilayer network-enabled IGDD (MNE-IGDD) monitoring approach. The contributions of the proposed method can be summarized into three aspects: (1) formulate the sequential ultrasound images to a multilayer network by the proposed spatial-regularized distance; (2) detect drug delivery area based on community detection algorithm of multilayer network; and (3) quantify the drug delivery progress by incorporating the image intensity-based features with the detected community. Both the detected communities and feature increment percentages are applied as the evaluation metric for validation. A simulation study was conducted and this method was also applied to a real-world mouse colon tumor treatment case study under three temperature conditions. Both simulation and the real-world case studies demonstrated that the proposed method is promising to achieve satisfactory monitoring performance in clinical trials.

Integral Curve Clustering and Simplification for Flow Visualization: A Comparative Evaluation

Unsupervised clustering techniques have been widely applied to flow simulation data to alleviate clutter and occlusion in the resulting visualization. However, there is an absence of systematic guidelines for users to evaluate (both quantitatively and visually) the appropriate clustering technique and similarity measures for streamline and pathline curves. In this work, we provide an overview of a number of prevailing curve clustering techniques. We then perform a comprehensive experimental study to qualitatively and quantitatively compare these clustering techniques coupled with popular similarity measures used in the flow visualization literature. Based on our experimental results, we derive empirical guidelines for selecting the appropriate clustering technique and similarity measure given the requirements of the visualization task. We believe our work will inform the task of generating meaningful reduced representations for large-scale flow data and inspire the continuous investigation of a more refined guidance on clustering technique selection.

ScatterNet: A Deep Subjective Similarity Model for Visual Analysis of Scatterplots

Similarity measuring methods are widely adopted in a broad range of visualization applications. In this work, we address the challenge of representing human perception in the visual analysis of scatterplots by introducing a novel deep-learning-based approach, ScatterNet, captures perception-driven similarities of such plots. The approach exploits deep neural networks to extract semantic features of scatterplot images for similarity calculation. We create a large labeled dataset consisting of similar and dissimilar images of scatterplots to train the deep neural network. We conduct a set of evaluations including performance experiments and a user study to demonstrate the effectiveness and efficiency of our approach. The evaluations confirm that the learned features capture the human perception of scatterplot similarity effectively. We describe two scenarios to show how ScatterNet can be applied in visual analysis applications.

Multi-objective Parameter Auto-tuning for Tissue Image Segmentation Workflows

We propose a software platform that integrates methods and tools for multi-objective parameter auto-tuning in tissue image segmentation workflows. The goal of our work is to provide an approach for improving the accuracy of nucleus/cell segmentation pipelines by tuning their input parameters. The shape, size, and texture features of nuclei in tissue are important biomarkers for disease prognosis, and accurate computation of these features depends on accurate delineation of boundaries of nuclei. Input parameters in many nucleus segmentation workflows affect segmentation accuracy and have to be tuned for optimal performance. This is a time-consuming and computationally expensive process; automating this step facilitates more robust image segmentation workflows and enables more efficient application of image analysis in large image datasets. Our software platform adjusts the parameters of a nuclear segmentation algorithm to maximize the quality of image segmentation results while minimizing the execution time. It implements several optimization methods to search the parameter space efficiently. In addition, the methodology is developed to execute on high-performance computing systems to reduce the execution time of the parameter tuning phase. These capabilities are packaged in a Docker container for easy deployment and can be used through a friendly interface extension in 3D Slicer. Our results using three real-world image segmentation workflows demonstrate that the proposed solution is able to (1) search a small fraction (about 100 points) of the parameter space, which contains billions to trillions of points, and improve the quality of segmentation output by × 1.20, × 1.29, and × 1.29, on average; (2) decrease the execution time of a segmentation workflow by up to 11.79× while improving output quality; and (3) effectively use parallel systems to accelerate parameter tuning and segmentation phases.

Algorithm sensitivity analysis and parameter tuning for tissue image segmentation pipelines

Motivation

Sensitivity analysis and parameter tuning are important processes in large-scale image analysis. They are very costly because the image analysis workflows are required to be executed several times to systematically correlate output variations with parameter changes or to tune parameters. An integrated solution with minimum user interaction that uses effective methodologies and high performance computing is required to scale these studies to large imaging datasets and expensive analysis workflows.

Results

The experiments with two segmentation workflows show that the proposed approach can (i) quickly identify and prune parameters that are non-influential; (ii) search a small fraction (about 100 points) of the parameter search space with billions to trillions of points and improve the quality of segmentation results (Dice and Jaccard metrics) by as much as 1.42× compared to the results from the default parameters; (iii) attain good scalability on a high performance cluster with several effective optimizations.

Conclusions

Our work demonstrates the feasibility of performing sensitivity analyses, parameter studies and auto-tuning with large datasets. The proposed framework can enable the quantification of error estimations and output variations in image segmentation pipelines.

Availability and Implementation

Source code: https://github.com/SBU-BMI/region-templates/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Comparative Local Quality Assessment of 3D Medical Image Segmentations with Focus on Statistical Shape Model-Based Algorithms

The quality of automatic 3D medical segmentation algorithms needs to be assessed on test datasets comprising several 3D images (i.e., instances of an organ). The experts need to compare the segmentation quality across the dataset in order to detect systematic segmentation problems. However, such comparative evaluation is not supported well by current methods. We present a novel system for assessing and comparing segmentation quality in a dataset with multiple 3D images. The data is analyzed and visualized in several views. We detect and show regions with systematic segmentation quality characteristics. For this purpose, we extended a hierarchical clustering algorithm with a connectivity criterion. We combine quality values across the dataset for determining regions with characteristic segmentation quality across instances. Using our system, the experts can also identify 3D segmentations with extraordinary quality characteristics. While we focus on algorithms based on statistical shape models, our approach can also be applied to cases, where landmark correspondences among instances can be established. We applied our approach to three real datasets: liver, cochlea and facial nerve. The segmentation experts were able to identify organ regions with systematic segmentation characteristics as well as to detect outlier instances.

Online topological segmentation of visual sequences using the algebraic connectivity of graphs

In the context of topological mapping, the automatic segmentation of an environment into meaningful and distinct locations is still regarded as an open problem. This paper presents an algorithm to extract places online from image sequences based on the algebraic connectivity of graphs or Fiedler value, which provides an insight into how well connected several consecutive observations are. The main contribution of the proposed method is that it is a theoretically supported alternative to tuning thresholds on similarities, which is a difficult task and environment dependent. It can accommodate any type of feature detector and matching procedure, as it only requires non-negative similarities as input, and is therefore able to deal with descriptors of variable length, to which statistical techniques are difficult to apply. The method has been validated in an office environment using exclusively visual information. Two different types of features, a bag-of-words model built from scale invariant feature transform (SIFT) keypoints, and a more complex fingerprint based on vertical lines, color histograms, and a few Star keypoints, are employed to demonstrate that the method can be applied to both fixed and variable length descriptors with similar results.

MotionFlow: Visual Abstraction and Aggregation of Sequential Patterns in Human Motion Tracking Data

Pattern analysis of human motions, which is useful in many research areas, requires understanding and comparison of different styles of motion patterns. However, working with human motion tracking data to support such analysis poses great challenges. In this paper, we propose MotionFlow, a visual analytics system that provides an effective overview of various motion patterns based on an interactive flow visualization. This visualization formulates a motion sequence as transitions between static poses, and aggregates these sequences into a tree diagram to construct a set of motion patterns. The system also allows the users to directly reflect the context of data and their perception of pose similarities in generating representative pose states. We provide local and global controls over the partition-based clustering process. To support the users in organizing unstructured motion data into pattern groups, we designed a set of interactions that enables searching for similar motion sequences from the data, detailed exploration of data subsets, and creating and modifying the group of motion patterns. To evaluate the usability of MotionFlow, we conducted a user study with six researchers with expertise in gesture-based interaction design. They used MotionFlow to explore and organize unstructured motion tracking data. Results show that the researchers were able to easily learn how to use MotionFlow, and the system effectively supported their pattern analysis activities, including leveraging their perception and domain knowledge.

Visual parameter optimisation for biomedical image processing

Background

Biomedical image processing methods require users to optimise input parameters to ensure high-quality output. This presents two challenges. First, it is difficult to optimise multiple input parameters for multiple input images. Second, it is difficult to achieve an understanding of underlying algorithms, in particular, relationships between input and output.

Results

We present a visualisation method that transforms users' ability to understand algorithm behaviour by integrating input and output, and by supporting exploration of their relationships. We discuss its application to a colour deconvolution technique for stained histology images and show how it enabled a domain expert to identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify deconvolution performance. It also enabled a breakthrough in understanding by invalidating an underlying assumption about the algorithm.

Conclusions

The visualisation method presented here provides analysis capability for multiple inputs and outputs in biomedical image processing that is not supported by previous analysis software. The analysis supported by our method is not feasible with conventional trial-and-error approaches.

Texture Image Segmentation Using Affinity Propagation and Spectral Clustering

Clustering is a popular and effective method for image segmentation. However, existing cluster methods often suffer the following problems: (1) Need a huge space and a lot of computation when the input data are large. (2) Need to assign some parameters (e.g. number of clusters) in advance which will affect the clustering results greatly. To save the space and computation, reduce the sensitivity of the parameters, and improve the effectiveness and efficiency of the clustering algorithms, we construct a new clustering algorithm for image segmentation. The new algorithm consists of two phases: coarsening clustering and exact clustering. First, we use Affinity Propagation (AP) algorithm for coarsening. Specifically, in order to save the space and computational cost, we only compute the similarity between each point and its t nearest neighbors, and get a condensed similarity matrix (with only t columns, where t << N and N is the number of data points). Second, to further improve the efficiency and effectiveness of the proposed algorithm, the Self-tuning Spectral Clustering (SSC) is used to the resulted points (the representative points gotten in the first phase) to do the exact clustering. As a result, the proposed algorithm can quickly and precisely realize the clustering for texture image segmentation. The experimental results show that the proposed algorithm is more efficient than the compared algorithms FCM, K-means and SOM.