Unsupervised segmentation of mitochondria using model-based spectral clustering

Segmentation of mitochondria in microscopic images represents a significant challenge that is motivated by the wide morphological and structural variations that are characteristic for this category of membrane enclosed sub cellular organelles. To address the drawbacks associated with manual mark-up procedures (which are common in current clinical evaluations), a recent direction of research investigate the application of statistical machine learning methods to mitochondria segmentation. Within this field of research the main issue was generated by the complexity of the training set that is able to describe the vast structural variation that is associated with mitochondria. To avoid this problem, in this paper we apply perceptual organization models such as Figure-Ground, Similarity, Proximity and Closure which target the identification of the closed membranes in EM images using multistage spectral clustering [1,2]. Our unsupervised mitochondria segmentation algorithm is outlined in Fig. 1. The first stage of the spectral clustering implements foreground segmentation with the similarity model S1 that aims to identify the dark contours that are given by the outer membrane of the mitochondrion. In the second stage, the foreground data is re-clustered with a different similarity model S2 to identify the inner membrane of the mitochondrion. The last stage involves a contour processing step that eliminates the pixels that are not consistent with the minimum distance between the inner and outer membranes of the mitochondrion. The algorithm has been tested on a suite of EM images provided by the American Society of Cell Biology and a number of experimental results are presented in Fig. 2

Cost-effective HPC clustering for computer vision applications

We will present a cost-effective and flexible realization of high performance computing (HPC) clustering and its potential in solving computationally intensive problems in computer vision. The featured software foundation to support the parallel programming is the GNU parallel Knoppix package with message passing interface (MPI) based Octave, Python and C interface capabilities. The implementation is especially of interest in applications where the main objective is to reuse the existing hardware infrastructure and to maintain the overall budget cost. We will present the benchmark results and compare and contrast the performances of Octave and MATLAB

A machine learning approach to the unsupervised segmentation of mitochondria in subcellular electron microscopy data

Recent advances in cellular and subcellular microscopy demonstrated its potential towards unravelling the mechanisms of various diseases at the molecular level. The biggest challenge in both human- and computer-based visual analysis of micrographs is the variety of nanostructures and mitochondrial morphologies. The state-of-the-art is, however, dominated by supervised manual data annotation and early attempts to automate the segmentation process were based on supervised machine learning techniques which require large datasets for training. Given a minimal number of training sequences or none at all, unsupervised machine learning formulations, such as spectral dimensionality reduction, are known to be superior in detecting salient image structures. This thesis presents three major contributions developed around the spectral clustering framework which is proven to capture perceptual organization features. Firstly, we approach the problem of mitochondria localization. We propose a novel grouping method for the extracted line segments which describes the normal mitochondrial morphology. Experimental findings show that the clusters obtained successfully model the inner mitochondrial membrane folding and therefore can be used as markers for the subsequent segmentation approaches. Secondly, we developed an unsupervised mitochondria segmentation framework. This method follows the evolutional ability of human vision to extrapolate salient membrane structures in a micrograph. Furthermore, we designed robust non-parametric similarity models according to Gestaltic laws of visual segregation. Experiments demonstrate that such models automatically adapt to the statistical structure of the biological domain and return optimal performance in pixel classification tasks under the wide variety of distributional assumptions. The last major contribution addresses the computational complexity of spectral clustering. Here, we introduced a new anticorrelation-based spectral clustering formulation with the objective to improve both: speed and quality of segmentation. The experimental findings showed the applicability of our dimensionality reduction algorithm to very large scale problems as well as asymmetric, dense and non-Euclidean datasets

Structural methods in subcellular image analysis

Mitochondria play a central role in cellular bioenergetics and in the regulation of apoptotic cell death. Mitochondrial morphology (shape and cristae architecture) is crucial to the understanding of apoptosis mechanisms and the subsequent development of therapies targeting age- and cancer-related diseases[1],[2]. There is a high demand in automated segmentation tools which can provide an objective quantitative information in a reasonable time frame[2]. The state of the art however is still dominated by manual segmentation tools[3]. Early attempts to address the challenges shown above are based on the machine learning framework[4]

Computer-guided recognition of mitochondria in densely cluttered subcellular environments

Mitochondria are membrane enclosed organelles, on average approximately 300nm in diameter, which reside inside every living cell. Mitochondria play a central role in cellular bioenergetics and in the regulation of apoptotic (programmed) cell death. They contain a family of pro-apoptotic BCL-2 proteins that interact with anti-apoptotic proteins to induce apoptosis. The measurement of the morphological changes that mitochondria undergo during experimentally induced apoptosis is crucial to the understanding of apoptosis mechanisms and for the subsequent development of therapies targeting age and cancer-related diseases. From a computer vision perspective, the Transmission Electron Microscope (TEM) images represent extremely complex and dynamic environments and pose considerable challenges on the automated localization and segmentation of mitochondria. This is due, in part, to the variety of subcellular organisms and the deformable nature of their shapes and textures. Furthermore, mitochondrial morphology depends on the type of biological tissue and undergo changes during induced or naturally occurring biochemical processes. TEM images of mitochondria are able to capture the intrinsic structural elements that are caused by the inner membrane folding. This evidence suggests the feasibility of a feature-driven recognition approach.Therefore,our work is focused on the quantitative analysis of mitochondrial morphology and on the development of algorithms that perform the localization and segmentation of mitochondria in TEMimages.We report initial success on the extraction of specific localization markers for categories of mitochondria with ormal, lamellar and tubular morphology. Future work will focus on the development of shape segmentation algorithms

Towards unsupervised segmentation in high-resolution medical nano-imaging

Recent advances in cellular and subcellular microscopy demonstrated its potential towards unraveling the mechanisms of various diseases at the molecular level. From a computer vision perspective nano-imaging is an inherently complex environment as can for example be seen from Fig.1(a,c). For the image analysis of intracellular organisms in high-resolution microscopy, new techniques which are capable of handling high-throughput data in a single pass and real time are of special interest. The additional emphasis is put therein on automated solutions which can provide the objective quantitative information in a reasonable time frame. The state-of-the-art is dominated by manual data annotation[1]and the early attempts to automate the segmentation are based on statistical machine-learning techniques[4]

Good bot vs. bad bot: Opportunities and consequences of using automated software in corporate communications

The paper attempts to lay a foundation for research on the use of bots in corporate communications. The first step is to identify opportunities and challenges that may offer starting points for future regulations. In this research project, expert interviews were conducted in the form of guideline-based telephone interviews. A total of ten experts from the scientific community and experts from the practical field were interviewed. Following this, a qualitative-reductive content analysis was conducted with the aim of building categories and hypotheses based on them. The results show that experts from the scientific community and practical field clearly see advantages for corporate communications, but also highlight hurdles and ethical challenges that are currently seen as a major barrier to the use of bots. In this context, experts mention, among other things, the assumption of structured routine tasks, ensuring efficiency and quality in corporate communications, cost efficiency and relieving employees. On the other hand, weaknesses, like the lack of transparency, data protection and loss of control arise. Results clearly show that the ethical perspective has to be taken into account. In this context, data protection, the question of responsibility and possible manipulation intentions are particularly worth mentioning

How important are faces for person re-identification?

This paper investigates the dependence of existing state-of-the-art person re-identification models on the presence and visibility of human faces. We apply a face detection and blurring algorithm to create anonymized versions of several popular person re-identification datasets including Market1501, DukeMTMC-reID, CUHK03, Viper, and Airport. Using a cross-section of existing state-of-the-art models that range in accuracy and computational efficiency, we evaluate the effect of this anonymization on re-identification performance using standard metrics. Perhaps surprisingly, the effect on mAP is very small, and accuracy is recovered by simply training on the anonymized versions of the data rather than the original data. These findings are consistent across multiple models and datasets. These results indicate that datasets can be safely anonymized by blurring faces without significantly impacting the performance of person reidentification systems, and may allow for the release of new richer re-identification datasets where previously there were privacy or data protection concerns

Improving person re-identification with temporal constraints

In this paper we introduce an image-based person re-identification dataset collected across five non-overlapping camera views in the large and busy airport in Dublin, Ireland. Unlike all publicly available image-based datasets, our dataset contains timestamp information in addition to frame number, and camera and person IDs. Also our dataset has been fully anonymized to comply with modern data privacy regulations. We apply state-of-the-art person re-identification models to our dataset and show that by leveraging the available timestamp information we are able to achieve a significant gain of 37.43% in mAP and a gain of 30.22% in Rank1 accuracy. We also propose a Bayesian temporal re-ranking post-processing step, which further adds a 10.03% gain in mAP and 9.95% gain in Rank1 accuracy metrics. This work on combining visual and temporal information is not possible on other image-based person re-identification datasets. We believe that the proposed new dataset will enable further development of person re-identification research for challenging real-world applications

Beyond social distancing: application of real-world coordinates in a multi-camera system with privacy protection

In this paper, we develop a privacy-preserving framework to detect and track pedestrians and project to their real-world coordinates facilitating social distancing detection. The transform is calculated using social distancing markers or floor tiles visible in the camera view, without an extensive calibration process. We select a lightweight detection model to process CCTV videos and perform tracking within-camera. The features collected during within-camera tracking are then used to associate passenger trajectories across multiple cameras. We demonstrate and analyze results qualitatively for both social distancing detection and multi-camera tracking on real-world data captured in a busy airport in Dublin, Ireland