24 papers · ranked by Valyu relevance
Iman Aganj, Mukesh G. Harisinghani, Ralph Weissleder, Bruce Fischl
Image segmentation is a critical step in numerous medical imaging studies, which can be facilitated by automatic computational techniques. Supervised methods, although highly effective, require large training datasets of manually labeled images that are labor-intensive to produce. Unsupervised methods, on the contrary…
M. Curie, Paulo da Costa
We introduce CLASP (Clustering via Adaptive Spectral Processing), a lightweight framework for unsupervised image segmentation that operates without any labeled data or finetuning. CLASP first extracts per patch features using a self supervised ViT encoder (DINO); then, it builds an affinity matrix and applies spectral…
Bogdan Kochetov, Phoenix Bell, Paulo S. Garcia, Akram S. Shalaby + 12 more
Multiplexed imaging technologies have made it possible to interrogate complex tumor microenvironments at sub-cellular resolution within their native spatial context. However, proper quantification of this complexity requires the ability to easily and accurately segment cells into their sub-cellular compartments. Within…
Wonjik Kim, Asako Kanezaki, Masayuki Tanaka
—The usage of convolutional neural networks (CNNs) for unsupervised image segmentation was investigated in this study. Similar to supervised image segmentation, the proposed CNN assigns labels to pixels that denote the cluster to which the pixel belongs. In unsupervised image segmentation, however, no training images…
Dantong Niu, Xudong Wang, Xinyang Han, Long Lian + 2 more
The field of image segmentation has witnessed significant advancements in the recent years . Nonetheless, the effectiveness of these segmentation methods heavily depends on the availability of extensive densely human-labeled data for training these models, * Equal Contribution. † Project Lead. which is both…
Shereen Fouad, David Randell, Antony Galton, Hisham Mehanna + 2 more
'Gabriel Landini' 'Dalin Tang'] Algorithmic segmentation of histologically relevant regions of tissues in digitized histopathological images is a critical step towards computer-assisted diagnosis and analysis. For example, automatic identification of epithelial and stromal tissues in images is important for spatial…
Paulo Victor dos Santos, Marcella Scoczynski Ribeiro Martins, Solange Amorim Nogueira, Cristhiane Gonçalves + 3 more
'Solange Amorim Nogueira' 'Cristhiane Gonçalves' 'Rafael Maffei Loureiro' 'Wesley Pacheco Calixto' 'Ihssan S. Masad'] This article presents an unsupervised method for segmenting brain computed tomography scans. The proposed methodology involves image feature extraction and application of similarity and continuity…
Ashif Sikandar Iquebal, Satish Bukkapatnam
—Advances in the image-based diagnostics of complex biological and manufacturing processes have brought unsupervised image segmentation to the forefront of enabling automated, on the fly decision making. However, most existing unsupervised segmentation approaches are either computationally complex or require manual…
Stephan Ihle, Andreas M. Reichmuth, Sophie Girardin, Hana Han + 5 more
The segmentation of images is a common task in a broad range of research fields. To tackle increasingly complex images, artificial intelligence (AI) based approaches have emerged to overcome the shortcomings of traditional feature detection methods. Owing to the fact that most AI research is made publicly accessible…
Sadia Basar, Mushtaq Ali, Gilberto Ochoa-Ruiz, Mahdi Zareei + 3 more
'Abdul Waheed' 'Awais Adnan' 'Mudassar Raza'] Color-based image segmentation classifies pixels of digital images in numerous groups for further analysis in computer vision, pattern recognition, image understanding, and image processing applications. Various algorithms have been developed for image segmentation, but…
Aleksandar Dimitriev, Matej Kristan
We propose a novel unsupervised image segmentation algorithm, which aims to segment an image into several coherent parts. It requires no user input, no supervised learning phase and assumes an unknown number of segments. It achieves this by first over-segmenting the image into several hundred superpixels. These are…
Maria K. Jaakkola, Maria Rantala, Anna Jalo, Teemu Saari + 9 more
Clustering time activity curves of PET images has been used to separate clinically relevant areas of the brain or tumours. However, such applications on segmenting PET images in organ level are much less studied due to the available total-body data being limited to animal studies. Now the new PET scanners providing the…
Clément Royer, Jérémie Sublime, Florence Rossant, Michel Paques + 2 more
'Vasudevan Lakshminarayanan' 'P. Jidesh'] Age-related macular degeneration (ARMD), a major cause of sight impairment for elderly people, is still not well understood despite intensive research. Measuring the size of the lesions in the fundus is the main biomarker of the severity of the disease and as such is widely…
Mariana Belgiu, Lucian Drǎguţ
Although multiresolution segmentation (MRS) is a powerful technique for dealing with very high resolution imagery, some of the image objects that it generates do not match the geometries of the target objects, which reduces the classification accuracy. MRS can, however, be guided to produce results that approach the…
Boujemaa Guermazi, Riadh Ksantini, Naimul Khan
—Image segmentation is the foundation of several computer vision tasks, where pixel-wise knowledge is a prerequisite for achieving the desired target. Deep learning has shown promising performance in supervised image segmentation. However, supervised segmentation algorithms require a massive amount of data annotated at…
Eleftheria A Mylona, Michalis A Savelonas, Dimitris Maroulis
This work introduces a novel framework for unsupervised parameterization of region-based active contour regularization and data fidelity terms, which is applied for medical image segmentation. The work aims to relieve MDs from the laborious, time-consuming task of empirical parameterization and bolster the objectivity…
Csaba Dávid, Kristóf Giber, Katalin Kerti-Szigeti, Mihaly Kollo + 2 more
Unsupervised segmentation in biological and non-biological images is only partially resolved. Segmentation either requires arbitrary thresholds or large teaching datasets. Here we propose a spatial autocorrelation method based on Local Moran’s I coefficient to differentiate signal, background and noise in any type of…
Yuhang Lu, Youchuan Wan, Gang Li
Unsupervised evaluation of segmentation quality is a crucial step in image segmentation applications. Previous unsupervised evaluation methods usually lacked the adaptability to multi-scale segmentation. A scale-constrained evaluation method that evaluates segmentation quality according to the specified target scale is…
Matthew R Whiteway, Evan S Schaffer, Anqi Wu, E Kelly Buchanan + 3 more
A popular approach to quantifying animal behavior from video data is through discrete behavioral segmentation, wherein video frames are labeled as containing one or more behavior classes such as walking or grooming. Sequence models learn to map behavioral features extracted from video frames to discrete behaviors, and…
Authors not listed
X-ray diffraction (XRD) is an immediate and powerful characterization technique that provides detailed information on the lattice structure and long-range order in crystalline materials. In recent decades, the quality and quantity of available crystal structure data has exploded, in large part due to the advent of…
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…
Hang Hu, Jyothsna Padmakumar Bindu, Julia Laskin
Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. However, complex MSI data are too large for manual annotation but too small for training deep…
Authors not listed
The analysis of nonadiabatic molecular dynamics (NAMD) data presents significant challenges due to its high dimensionality and complexity. To address these issues, we introduce ULaMDyn, a Python-based, open-source package designed to automate the unsupervised analysis of large datasets generated by NAMD simulations.…
Authors not listed
Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR) spectral images, without relying on prior chemical knowledge. The potential of machine learning for chemical…