27 papers · ranked by Valyu relevance
Sandra Jardim, João António, Carlos Mora, Xiaohao Cai + 2 more
'Gaohang Yu'] With a wide range of applications, image segmentation is a complex and difficult preprocessing step that plays an important role in automatic visual systems, which accuracy impacts, not only on segmentation results, but directly affects the effectiveness of the follow-up tasks. Despite the many advances…
Tridib K. Biswas, Jonathan Vacher, Sophie Molholm, Pascal Mamassian + 1 more
The visual system operates by segmenting visual inputs into distinct perceptual objects. Segmentation is dynamic, as revealed by the tempo of perceptual choices and neural activity in visual cortex. Dynamics for natural stimuli however, are poorly understood because natural scene segmentation is ambiguous and…
Elaine ML Ho, Dimitrios Ladakis, Mark Basham, Michele C Darrow
Segmentation of biological images identifies regions of an image which correspond to specific features of interest, which can be analysed quantitatively to answer biological questions. This task has long been a barrier to conducting large-scale biological imaging studies as it is time- and labour-intensive. Modern…
Zhenyu Zhao, Yan He, Miao Chen
—Online controlled experimentation is widely adopted for evaluating new features in the rapid development cycle for web products and mobile applications. Measurement on overall experiment sample is a common practice to quantify the overall treatment effect. In order to understand why the treatment effect occurs in a…
Raffaella Fiamma Cabini, Anna Pichiecchio, Alessandro Lascialfari, Silvia Figini + 1 more
'Silvia Figini' 'Mattia Zanella'] In this work, we apply a kinetic version of a bounded confidence consensus model to biomedical segmentation problems. In the presented approach, time-dependent information on the microscopic state of each particle/pixel includes its space position and a feature representing a static…
Yichen He, Marco Camaiti, Lucy E. Roberts, James M. Mulqueeney + 2 more
The increased availability of 3D image data requires improving the efficiency of digital segmentation, currently relying on manual labelling, especially when separating structures into multiple components. Automated and semi-automated methods to streamline segmentation have been developed, such as deep learning and…
Elyas Heidari, Andrew Moorman, Dániel Unyi, Nikhita Pasnuri + 8 more
The accurate assignment of transcripts to their cells of origin remains the Achilles heel of imaging-based spatial transcriptomics, despite being critical for nearly all downstream analyses. Current cell segmentation methods are prone to over- and under-segmentation, misassign transcripts to cells, require manual…
Jeová Farias Sales Rocha Neto
Image Segmentation is one of the core tasks in Computer Vision, and solving it often depends on modeling the image appearance data via the color distributions of each of its constituent regions. Whereas many segmentation algorithms handle the appearance model dependence using alternation or implicit methods, we propose…
Alexander E. Siemenn, Eunice Aissi, Fang Sheng, Armi Tiihonen + 3 more
In materials research, the task of characterizing hundreds of different materials traditionally requires equally many human hours spent measuring samples one by one. We demonstrate that with the integration of computer vision into this material research workflow, many of these tasks can be automated, significantly…
Laura Wiggins, Peter J. O’Toole, William J. Brackenbury, Julie Wilson
Segmentation and tracking are essential preliminary steps in the analysis of almost all live cell imaging applications. Although the number of open-source software systems that facilitate automated segmentation and tracking continue to evolve, many researchers continue to opt for manual alternatives for samples that…
Taymaz Akan, Amin Golzari Oskouei, Sait Alp, Mohammad Alfrad Nobel Bhuiyan
'Mohammad Alfrad Nobel Bhuiyan'] One of the highly focused areas in the medical science community is segmenting tumors from brain magnetic resonance imaging (MRI). The diagnosis of malignant tumors at an early stage is necessary to provide treatment for patients. The patient’s prognosis will improve if it is detected…
Yonghao Zhao, Zhouyuan Zhu, Sen Yang, Weihan Li
An essential step for quantitative image analysis is cell segmentation, which is the process of defining the outline of individual cells in microscopy images. Segmentation of budding yeast is challenging due to their asymmetric cell division and mother-bud morphology. As a result, a dividing cell is frequently…
Kaustubh Shivshankar Shejole, Gaurav Mishra
—Interactive graph-based segmentation methods partition an image into foreground and background regions with the aid of user inputs. However, existing approaches often suffer from high computational costs, sensitivity to user interactions, and degraded performance when the foreground and background share similar color…
Zhenzhou Wang
The histogram of an image is the accurate graphical representation of the numerical grayscale distribution and it is also an estimate of the probability distribution of image pixels. Therefore, histogram has been widely adopted to calculate the clustering means and partitioning thresholds for image segmentation. There…
Mariusz Marzec, Adam Piórkowski, Arkadiusz Gertych
Background High-content screening (HCS) is a pre-clinical approach for the assessment of drug efficacy. On modern platforms, it involves fluorescent image capture using three-dimensional (3D) scanning microscopy. Segmentation of cell nuclei in 3D images is an essential prerequisite to quantify captured fluorescence in…
Guochang Ye, Mehmet Kaya, Larbi Boubchir, Charles Tijus + 3 more
Cell segmentation is a critical step for image-based experimental analysis. Existing cell segmentation methods are neither entirely automated nor perform well under basic laboratory microscopy. This study proposes an efficient and automated cell segmentation method involving morphological operations to automatically…
Jonathan Vacher, Claire Launay, Pascal Mamassian, Ruben Coen-Cagli + 1 more
Segmenting visual stimuli into distinct groups of features and visual objects is central to visual function. Classical psychophysical methods have helped uncover many rules of human perceptual segmentation, and recent progress in machine learning has produced successful algorithms. Yet, the computational logic of human…
Giuseppe Giacopelli, Michele Migliore, Domenico Tegolo, Marc Brecht
Background: Image analysis applications in digital pathology include various methods for segmenting regions of interest. Their identification is one of the most complex steps and therefore of great interest for the study of robust methods that do not necessarily rely on a machine learning (ML) approach. Method: A fully…
Mingjiang Li, Jincheng Zhou, Dan Wang, Peng Peng + 1 more
The segmentation of brain tissue by MRI not only contributes to the study of the function and anatomical structure of the brain, but it also offers a theoretical foundation for the diagnosis and treatment of brain illnesses. When discussing the anatomy of the brain in a clinical setting, the terms “white matter,” “gray…
André Luiz Corrêa Vianna Filho, Leonardo de Lima, Mariana Kleina
The present article proposes a graph-based approach to customer segmentation, combining the RFM analysis with the classical optimization max-k-cut problem. We consider each customer as a vertex of a weighted graph, and the edge weights are given by the distances between the vectors corresponding to the (R, F, M)-scores…
Jordão Bragantini, Merlin Lange, Loïc A. Royer
In this work, we describe a method for large-scale 3D celltracking through a segmentation selection approach. The proposed method is effective at tracking cells across large microscopy datasets on two fronts: (i) It can solve problems containing millions of segmentation instances in terabyte-scale 3D+t datasets; (ii)…
Rama El-khawaldeh, Mason Guy, Finn Bork, Nina Taherimakhsousi + 6 more
This work presents a generalizable computer vision (CV) and machine learning model that is used for automated real-time monitoring and control of a diverse array of workup processes. Our system simultaneously monitors multiple physical parameters (e.g., liquid level, homogeneity, turbidity, solid, residue, and color)…
Felix Bänsch, Jonas Schaub, Betül Sevindik, Samuel Behr + 3 more
Developing and implementing computational algorithms for the extraction of specific substructures from molecular graphs (in silico molecule fragmentation) is an iterative process. It involves repeated sequences of implementing a rule set, applying it to relevant structural data, checking the results, and adjusting the…
Judah Evangelista, Michael S. Kay
Chemical protein synthesis (CPS), in which custom peptide segments of ~20-60 aa are produced by solid-phase peptide synthesis and then stitched together through sequential ligation reactions, is an increasingly popular technique. The workflow of CPS is often depicted with a “bracket” style diagram detailing the…
Romain Karpinski, Alice Gros, Marc Karnat, Qazi Saaheelur Rahaman + 4 more
We propose a two-stage supervised framework for characterizing cell divisions in 2D and 3D time-lapse microscopy. First, we recast division detection as a semantic segmentation task on image sequences. Second, a local regression model estimates the orientation and distance between daughter cells for each event. We…
Thomas Lynn, Julio Ottino, Richard Lueptow, Paul Umbanhowar
Cut-and-shuffle mixing is an instructive candidate system with which to assess the potential of machine learning (ML) as an approach to solve difficult mixing problems. We focus on a specific subset of cut-and-shuffle systems, the one-dimensional interval exchange transform. This class of mixing operations is well…
Babu Bassa
In this communication the author describes a software tool named "ChameleonSort". The software program, developed by the present author is useful in the sorting of biological sequence variants like those accumulating mutations while diverging from the common ancestors. Examples include viral protein variants, protein…