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Relevant bibliographies by topics / SEGMENTATION SYSTEM

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Journal articles

Academic literature on the topic 'SEGMENTATION SYSTEM'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "SEGMENTATION SYSTEM"

1

Liu, Qiming, Qifan Lu, Yezi Chai, et al. "Radiomics-Based Quality Control System for Automatic Cardiac Segmentation: A Feasibility Study." Bioengineering 10, no. 7 (2023): 791. http://dx.doi.org/10.3390/bioengineering10070791.

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Purpose: In the past decade, there has been a rapid increase in the development of automatic cardiac segmentation methods. However, the automatic quality control (QC) of these segmentation methods has received less attention. This study aims to address this gap by developing an automatic pipeline that incorporates DL-based cardiac segmentation and radiomics-based quality control. Methods: In the DL-based localization and segmentation part, the entire heart was first located and cropped. Then, the cropped images were further utilized for the segmentation of the right ventricle cavity (RVC), myo

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Meenakshi BK, Meenakshi BK, and Prasad M. R. Prasad M R. "Survey on Segmentation to Iris Recognition System." International Journal of Scientific Research 3, no. 4 (2012): 514–15. http://dx.doi.org/10.15373/22778179/apr2014/184.

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Gurari, Danna, Mehrnoosh Sameki, and Margrit Betke. "Investigating the Influence of Data Familiarity to Improve the Design of a Crowdsourcing Image Annotation System." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 4 (September 21, 2016): 59–68. http://dx.doi.org/10.1609/hcomp.v4i1.13294.

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Crowdsourced demarcations of object boundaries in images (segmentations) are important for many vision-based applications. A commonly reported challenge is that a large percentage of crowd results are discarded due to concerns about quality. We conducted three studies to examine (1) how does the quality of crowdsourced segmentations differ for familiar everyday images versus unfamiliar biomedical images?, (2) how does making familiar images less recognizable (rotating images upside down) influence crowd work with respect to the quality of results, segmentation time, and segmentation detail?, a

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Mendoza Garay, Juan Ignacio. "Segmentation boundaries in accelerometer data of arm motion induced by music: Online computation and perceptual assessment." Human Technology 18, no. 3 (2022): 250–66. http://dx.doi.org/10.14254/1795-6889.2022.18-3.4.

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Segmentation is a cognitive process involved in the understanding of information perceived through the senses. Likewise, the automatic segmentation of data captured by sensors may be used for the identification of patterns. This study is concerned with the segmentation of dancing motion captured by accelerometry and its possible applications, such as pattern learning and recognition, or gestural control of devices. To that effect, an automatic segmentation system was formulated and tested. Two participants were asked to ‘dance with one arm’ while their motion was measured by an accelerometer.

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Sunoqrot, Mohammed R. S., Kirsten M. Selnæs, Elise Sandsmark, et al. "A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI." Diagnostics 10, no. 9 (2020): 714. http://dx.doi.org/10.3390/diagnostics10090714.

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Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI. Four different deep learning-based segmentation methods were used to segment the prostate for 585

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Joseph, E., A. M. Aibinu, B. A. Sadiq, H. Bello Salau, and M. J. E. Salami. "Scorpion image segmentation system." IOP Conference Series: Materials Science and Engineering 53 (December 20, 2013): 012055. http://dx.doi.org/10.1088/1757-899x/53/1/012055.

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Mauricaite, Radvile, Ella Mi, Jiarong Chen, Andrew Ho, Lillie Pakzad-Shahabi, and Matthew Williams. "Fully automated deep learning system for detecting sarcopenia on brain MRI in glioblastoma." Neuro-Oncology 23, Supplement_4 (2021): iv13. http://dx.doi.org/10.1093/neuonc/noab195.031.

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Abstract Aims Glioblastoma multiforme (GBM) is an aggressive brain malignancy. Performance status is an important prognostic factor but is subjectively evaluated, resulting in inaccuracy. Objective markers of frailty/physical condition, such as measures of skeletal muscle mass can be evaluated on cross-sectional imaging and is associated with cancer survival. In GBM, temporalis muscle has been identified as a skeletal muscle mass surrogate and a prognostic factor. However, current manual muscle quantification is time consuming, limiting clinical adoption. We previously developed a deep learnin

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Wang, Zhengfei, Lai Wang, Fu'an Xiao, Qingsong Chen, Liming Lu, and Jiaming Hong. "A Traditional Chinese Medicine Traceability System Based on Lightweight Blockchain." Journal of Medical Internet Research 23, no. 6 (2021): e25946. http://dx.doi.org/10.2196/25946.

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Background Recently, the problem of traditional Chinese medicine (TCM) safety has attracted attention worldwide. To prevent the spread of counterfeit drugs, it is necessary to establish a drug traceability system. A traditional drug traceability system can record the whole circulation process of drugs, from planting, production, processing, and warehousing to use by hospitals and patients. Once counterfeit drugs are found, they can be traced back to the source. However, traditional drug traceability systems have some drawbacks, such as failure to prevent tampering and facilitation of sensitive

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Allioui, Hanane, Mohamed Sadgal, and Aziz El Fazziki. "An Improved Image Segmentation System." Journal of communications software and systems 16, no. 2 (2020): 143–55. http://dx.doi.org/10.24138/jcomss.v16i2.830.

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In this paper, we present a solution-based cooperation approach for strengthening the image segmentation.This paper proposes a cooperative method relying on Multi-Agent System. The main contribution of this work is to highlight the importance of cooperation between the contour and region growing based on Multi-Agent System (MAS). Consequently, agents’ interactions form the main part of the whole process for image segmentation. Similar works were proposed to evaluate the effectiveness of the proposed solution. The main difference is that our Multi-Agent System can perform the segmentation proce

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10

Baujard, Olivier. "KISS: a multiagent segmentation system." Optical Engineering 32, no. 6 (1993): 1235. http://dx.doi.org/10.1117/12.134190.

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