scholarly journals Object-Guided Instance Segmentation for Biological Images

2020 ◽  
Vol 34 (07) ◽  
pp. 12677-12684
Author(s):  
Jingru Yi ◽  
Hui Tang ◽  
Pengxiang Wu ◽  
Bo Liu ◽  
Daniel J. Hoeppner ◽  
...  
Keyword(s):  
Target Object ◽  
Plant Phenotyping ◽  
Neural Cells ◽  
Cell Nuclei ◽  
Biological Images ◽  
Segmentation Methods ◽  
Level Information ◽  
Bounding Boxes ◽  
The Individual ◽  
Instance Segmentation

Instance segmentation of biological images is essential for studying object behaviors and properties. The challenges, such as clustering, occlusion, and adhesion problems of the objects, make instance segmentation a non-trivial task. Current box-free instance segmentation methods typically rely on local pixel-level information. Due to a lack of global object view, these methods are prone to over- or under-segmentation. On the contrary, the box-based instance segmentation methods incorporate object detection into the segmentation, performing better in identifying the individual instances. In this paper, we propose a new box-based instance segmentation method. Mainly, we locate the object bounding boxes from their center points. The object features are subsequently reused in the segmentation branch as a guide to separate the clustered instances within an RoI patch. Along with the instance normalization, the model is able to recover the target object distribution and suppress the distribution of neighboring attached objects. Consequently, the proposed model performs excellently in segmenting the clustered objects while retaining the target object details. The proposed method achieves state-of-the-art performances on three biological datasets: cell nuclei, plant phenotyping dataset, and neural cells.

scholarly journals Efficient Stain-Aware Nuclei Segmentation Deep Learning Framework for Multi-Center Histopathological Images

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 954
Author(s):  
Loay Hassan ◽  
Mohamed Abdel-Nasser ◽  
Adel Saleh ◽  
Osama A. Omer ◽  
Domenec Puig
Keyword(s):  
Deep Learning ◽  
Choquet Integral ◽  
Adjacent Cell ◽  
Cell Nuclei ◽  
Nuclei Segmentation ◽  
Learning Framework ◽  
Segmentation Methods ◽  
Histopathological Images ◽  
The Individual ◽  
Whole Slide Images

Existing nuclei segmentation methods have obtained limited results with multi-center and multi-organ whole-slide images (WSIs) due to the use of different stains, scanners, overlapping, clumped nuclei, and the ambiguous boundary between adjacent cell nuclei. In an attempt to address these problems, we propose an efficient stain-aware nuclei segmentation method based on deep learning for multi-center WSIs. Unlike all related works that exploit a single-stain template from the dataset to normalize WSIs, we propose an efficient algorithm to select a set of stain templates based on stain clustering. Individual deep learning models are trained based on each stain template, and then, an aggregation function based on the Choquet integral is employed to combine the segmentation masks of the individual models. With a challenging multi-center multi-organ WSIs dataset, the experimental results demonstrate that the proposed method outperforms the state-of-art nuclei segmentation methods with aggregated Jaccard index (AJI) and F1-scores of 73.23% and 89.32%, respectively, while achieving a lower number of parameters.

scholarly journals A Deep Detection Network Based on Interaction of Instance Segmentation and Object Detection for SAR Images

2021 ◽  
Vol 13 (13) ◽  
pp. 2582
Author(s):  
Zitong Wu ◽  
Biao Hou ◽  
Bo Ren ◽  
Zhongle Ren ◽  
Shuang Wang ◽  
...  
Keyword(s):  
Object Detection ◽  
Multiple Scales ◽  
Interaction Space ◽  
Sar Images ◽  
Ship Detection ◽  
Detection Algorithms ◽  
Level Information ◽  
Bounding Boxes ◽  
The Relationship ◽  
Instance Segmentation

Ship detection is a challenging task for synthetic aperture radar (SAR) images. Ships have arbitrary directionality and multiple scales in SAR images. Furthermore, there is a lot of clutter near the ships. Traditional detection algorithms are not robust to these situations and easily cause redundancy in the detection area. With the continuous improvement in resolution, the traditional algorithms cannot achieve high-precision ship detection in SAR images. An increasing number of deep learning algorithms have been applied to SAR ship detection. In this study, a new ship detection network, known as the instance segmentation assisted ship detection network (ISASDNet), is presented. ISASDNet is a two-stage detection network with two branches. A branch is called an object branch and can extract object-level information to obtain positioning bounding boxes and classification results. Another branch called the pixel branch can be utilized for instance segmentation. In the pixel branch, the designed global relational inference layer maps the features to interaction space to learn the relationship between ship and background. The global reasoning module (GRM) based on global relational inference layers can better extract the instance segmentation results of ships. A mask assisted ship detection module (MASDM) is behind the two branches. The MASDM can improve detection results by interacting with the outputs of the two branches. In addition, a strategy is designed to extract the mask of SAR ships, which enables ISASDNet to perform object detection training and instance segmentation training at the same time. Experiments carried out two different datasets demonstrated the superiority of ISASDNet over other networks.

scholarly journals Panoptic Feature Fusion Net: A Novel Instance Segmentation Paradigm for Biomedical and Biological Images

2021 ◽  
Vol 30 ◽  
pp. 2045-2059
Author(s):  
Dongnan Liu ◽  
Donghao Zhang ◽  
Yang Song ◽  
Heng Huang ◽  
Weidong Cai
Keyword(s):  
Feature Fusion ◽  
Biological Images ◽  
Instance Segmentation

scholarly journals Interpersonal Comparisons of the Good: Epistemic not Impossible

Utilitas ◽  
2015 ◽  
Vol 28 (3) ◽  
pp. 288-313 ◽  
Author(s):  
MATHEW COAKLEY
Keyword(s):  
Institutional Choice ◽  
Individual Level ◽  
Interpersonal Comparisons ◽  
Individual Good ◽  
Level Information ◽  
Action Policy ◽  
Social Mind ◽  
The Individual

To evaluate the overall good/welfare of any action, policy or institutional choice we need some way of comparing the benefits and losses to those affected: we need to make interpersonal comparisons of the good/welfare. Yet sceptics have worried either: (1) that such comparisons are impossible as they involve an impossible introspection across individuals, getting ‘into their minds’; (2) that they are indeterminate as individual-level information is compatible with a range of welfare numbers; or (3) that they are metaphysically mysterious as they assume the existence either of a social mind or of absolute levels of welfare when no such things exist. This article argues that such scepticism can potentially be addressed if we view the problem of interpersonal comparisons as fundamentally an epistemic problem – that is, as a problem of forming justified beliefs about the overall good based on evidence of the individual good.

scholarly journals Automated Filtering of Eye Movements Using Dynamic AOI in Multiple Granularity Levels

2021 ◽  
Vol 12 (1) ◽  
pp. 49-64
Author(s):  
Gavindya Jayawardena ◽  
Sampath Jayarathna
Keyword(s):  
Eye Movements ◽  
Eye Tracking ◽  
Eye Movement ◽  
Eye Gaze ◽  
Movement Analysis ◽  
Movement Data ◽  
Bounding Boxes ◽  
Multiple Granularity ◽  
Instance Segmentation ◽  
Segmentation Models

Eye-tracking experiments involve areas of interest (AOIs) for the analysis of eye gaze data. While there are tools to delineate AOIs to extract eye movement data, they may require users to manually draw boundaries of AOIs on eye tracking stimuli or use markers to define AOIs. This paper introduces two novel techniques to dynamically filter eye movement data from AOIs for the analysis of eye metrics from multiple levels of granularity. The authors incorporate pre-trained object detectors and object instance segmentation models for offline detection of dynamic AOIs in video streams. This research presents the implementation and evaluation of object detectors and object instance segmentation models to find the best model to be integrated in a real-time eye movement analysis pipeline. The authors filter gaze data that falls within the polygonal boundaries of detected dynamic AOIs and apply object detector to find bounding-boxes in a public dataset. The results indicate that the dynamic AOIs generated by object detectors capture 60% of eye movements & object instance segmentation models capture 30% of eye movements.

scholarly journals Accurate Instance Segmentation in Pediatric Elbow Radiographs

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7966
Author(s):  
Dixiao Wei ◽  
Qiongshui Wu ◽  
Xianpei Wang ◽  
Meng Tian ◽  
Bowen Li
Keyword(s):  
Elbow Joint ◽  
Segmentation Method ◽  
Qualitative And Quantitative ◽  
Robust Segmentation ◽  
Local Contexts ◽  
Good Potential ◽  
Quantitative Results ◽  
Bounding Boxes ◽  
Global And Local ◽  
Instance Segmentation

Radiography is an essential basis for the diagnosis of fractures. For the pediatric elbow joint diagnosis, the doctor needs to diagnose abnormalities based on the location and shape of each bone, which is a great challenge for AI algorithms when interpreting radiographs. Bone instance segmentation is an effective upstream task for automatic radiograph interpretation. Pediatric elbow bone instance segmentation is a process by which each bone is extracted separately from radiography. However, the arbitrary directions and the overlapping of bones pose issues for bone instance segmentation. In this paper, we design a detection-segmentation pipeline to tackle these problems by using rotational bounding boxes to detect bones and proposing a robust segmentation method. The proposed pipeline mainly contains three parts: (i) We use Faster R-CNN-style architecture to detect and locate bones. (ii) We adopt the Oriented Bounding Box (OBB) to improve the localizing accuracy. (iii) We design the Global-Local Fusion Segmentation Network to combine the global and local contexts of the overlapped bones. To verify the effectiveness of our proposal, we conduct experiments on our self-constructed dataset that contains 1274 well-annotated pediatric elbow radiographs. The qualitative and quantitative results indicate that the network significantly improves the performance of bone extraction. Our methodology has good potential for applying deep learning in the radiography’s bone instance segmentation.

Image Segmentation for Feature Extraction

2018 ◽  
pp. 232-257 ◽  
Author(s):  
C. Deisy ◽  
Mercelin Francis
Keyword(s):  
Plant Pathology ◽  
Feature Extraction ◽  
Disease Diagnosis ◽  
Target Object ◽  
Literature Survey ◽  
Remedial Measures ◽  
Disease Symptoms ◽  
Segmentation Methods ◽  
Agricultural Plants ◽  
Object Features

This chapter explores the prevailing segmentation methods to extract the target object features, in the field of plant pathology for disease diagnosis. The digital images of different plant leaves are taken for analysis as most of the disease symptoms are visible on leaves apart from other vital parts. Among the different phases of processing a digital image, the substantive focus of the study concentrates mainly on the methodology or algorithms deployed on image acquisition, preprocessing, segmentation, and feature extraction. The chapter collects the existing literature survey related to disease diagnosis methods in agricultural plants and prominently highlights the performance of each algorithm by comparing with its counterparts. The main aim is to provide an insight of creativeness to the researchers and experts to develop a less expensive, accurate, fast and an instant system for the timely detection of plant disease, so that appropriate remedial measures can be taken.

scholarly journals Emotion and liking: how director emotional expression and knowledge of (dis)liking may impact adults’ ability to follow the instructions of an ignorant speaker

2020 ◽  
Author(s):  
Rebecca L. Monk ◽  
Lauren Colbert ◽  
Gemma Darker ◽  
Jade Cowling ◽  
Bethany Jones ◽  
...  
Keyword(s):  
Perspective Taking ◽  
Real Life ◽  
Target Object ◽  
Individual Level ◽  
Object Selection ◽  
Knowledge And Beliefs ◽  
Correct Object ◽  
The Individual ◽  
Key Aspects ◽  
Accuracy Rates

Abstract Background Theory of mind (ToM), the ability to understand that others have different knowledge and beliefs to ourselves, has been the subject of extensive research which suggests that we are not always efficient at taking another’s perspective, known as visual perspective taking (VPT). This has been studied extensively and a growing literature has explored the individual-level factors that may affect perspective taking (e.g. empathy and group membership). However, while emotion and (dis)liking are key aspects within everyday social interaction, research has not hitherto explored how these factors may impact ToM. Method A total of 164 participants took part in a modified director task (31 males (19%), M age = 20.65, SD age = 5.34), exploring how correct object selection may be impacted by another’s emotion (director facial emotion; neutral × happy × sad) and knowledge of their (dis)likes (i.e. director likes specific objects). Result When the director liked the target object or disliked the competitor object, accuracy rates were increased relative to when he disliked the target object or liked the competitor object. When the emotion shown by the director was incongruent with their stated (dis)liking of an object (e.g. happy when he disliked an object), accuracy rates were also increased. None of these effects were significant in the analysis of response time. These findings suggest that knowledge of liking may impact ToM use, as can emotional incongruency, perhaps by increasing the saliency of perspective differences between participant and director. Conclusion As well as contributing further to our understanding of real-life social interactions, these findings may have implications for ToM research, where it appears that more consideration of the target/director’s characteristics may be prudent.

scholarly journals The Cognitive Lens: a primer on conceptual tools for analysing information processing in developmental and regenerative morphogenesis

2019 ◽  
Vol 374 (1774) ◽  
pp. 20180369 ◽  
Author(s):  
Santosh Manicka ◽  
Michael Levin
Keyword(s):  
Information Processing ◽  
Evolutionary Biology ◽  
Biological Information ◽  
Neural Cells ◽  
Cognitive Architectures ◽  
Least Action ◽  
Mechanistic Investigation ◽  
Wide Range ◽  
Level Information ◽  
Scale Integration

Brains exhibit plasticity, multi-scale integration of information, computation and memory, having evolved by specialization of non-neural cells that already possessed many of the same molecular components and functions. The emerging field of basal cognition provides many examples of decision-making throughout a wide range of non-neural systems. How can biological information processing across scales of size and complexity be quantitatively characterized and exploited in biomedical settings? We use pattern regulation as a context in which to introduce the Cognitive Lens—a strategy using well-established concepts from cognitive and computer science to complement mechanistic investigation in biology. To facilitate the assimilation and application of these approaches across biology, we review tools from various quantitative disciplines, including dynamical systems, information theory and least-action principles. We propose that these tools can be extended beyond neural settings to predict and control systems-level outcomes, and to understand biological patterning as a form of primitive cognition. We hypothesize that a cognitive-level information-processing view of the functions of living systems can complement reductive perspectives, improving efficient top-down control of organism-level outcomes. Exploration of the deep parallels across diverse quantitative paradigms will drive integrative advances in evolutionary biology, regenerative medicine, synthetic bioengineering, cognitive neuroscience and artificial intelligence. This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.

scholarly journals Organization of Highly Acetylated Chromatin around Sites of Heterogeneous Nuclear RNA Accumulation

1998 ◽  
Vol 9 (9) ◽  
pp. 2491-2507 ◽  
Author(s):  
Michael J. Hendzel ◽  
Michael J. Kruhlak ◽  
David P. Bazett-Jones
Keyword(s):  
Histone Deacetylases ◽  
Active Regions ◽  
Fibroblast Cell ◽  
Cell Nuclei ◽  
Transmission Electron ◽  
Interchromatin Granule ◽  
Nuclear Rna ◽  
Interchromatin Granules ◽  
The Individual ◽  
Rna Accumulation

Histones found within transcriptionally competent and active regions of the genome are highly acetylated. Moreover, these highly acetylated histones have very short half-lives. Thus, both histone acetyltransferases and histone deacetylases must enrich within or near these euchromatic regions of the interphase chromatids. Using an antibody specific for highly acetylated histone H3, we have investigated the organization of transcriptionally active and competent chromatin as well as nuclear histone acetyltransferase and deacetylase activities. We observe an exclusion of highly acetylated chromatin around the periphery of the nucleus and an enrichment near interchromatin granule clusters (IGCs). The highly acetylated chromatin is found in foci that may reflect the organization of highly acetylated chromatin into “chromonema” fibers. Transmission electron microscopy of Indian muntjac fibroblast cell nuclei indicates that the chromatin associated with the periphery of IGCs remains relatively condensed, most commonly found in domains containing chromatin folded beyond 30 nm. Using electron spectroscopic imaging, we demonstrate that IGCs are clusters of ribonucleoprotein particles. The individual granules comprise RNA-rich fibrils or globular regions that fold into individual granules. Quantitative analysis of individual granules indicates that they contain variable amounts of RNA estimated between 1.5 and >10 kb. We propose that interchromatin granules are heterogeneous nuclear RNA-containing particles, some of which may be pre-mRNA generated by nearby transcribed chromatin. An intermediary zone between the IGC and surrounding chromatin is described that contains factors with the potential to provide specificity to the localization of sequences near IGCs.

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