scholarly journals Accurate cytogenetic biodosimetry through automation of dicentric chromosome curation and metaphase cell selection

2017 ◽  
Author(s):  
Jin Liu ◽  
Yanxin Li ◽  
Ruth Wilkins ◽  
Farrah Flegal ◽  
Joan H. Knoll ◽  
...  
Keyword(s):  
False Positive ◽  
Estimation Error ◽  
Digital Pathology ◽  
Image Data ◽  
Test Sample ◽  
Chromosome Morphology ◽  
Average Dose ◽  
High Quality ◽  
Metaphase Cell ◽  
Segmentation Methods

ABSTRACTSoftware to automate digital pathology relies on image quality and the rates of false positive and negative objects in these images. Cytogenetic biodosimetry detects dicentric chromosomes (DCs) that arise from exposure to ionizing radiation, and determines radiation dose received from the frequency of DCs. We present image segmentation methods to rank high quality cytogenetic images and eliminate suboptimal metaphase cell data based on novel quality measures. Improvements in DC recognition increase the accuracy of dose estimates, by reducing false positive (FP) DC detection. A set of chromosome morphology segmentation methods selectively filtered out false DCs, arising primarily from extended prometaphase chromosomes, sister chromatid separation and chromosome fragmentation. This reduced FPs by 55% and was highly specific to the abnormal structures (≥97.7%). Additional procedures were then developed to fully automate image review, resulting in 6 image-level filters that, when combined, selectively remove images with consistently unparsable or incorrectly segmented chromosome morphologies. Overall, these filters can eliminate half of the FPs detected by manual image review. Optimal image selection and FP DCs are minimized by combining multiple feature based segmentation filters and a novel image sorting procedure based on the known distribution of chromosome lengths. Applying the same segmentation filtering procedures to both calibration and test sample image data reduced the average dose estimation error from 0.4Gy to <0.2Gy, obviating the need to first manually review these images. This reliable and scalable solution enables batch processing multiple samples of unknown dose, and meets current requirements for triage radiation biodosimetry of high quality metaphase cell preparations.

scholarly journals Accurate cytogenetic biodosimetry through automated dicentric chromosome curation and metaphase cell selection

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1396 ◽  
Author(s):  
Jin Liu ◽  
Yanxin Li ◽  
Ruth Wilkins ◽  
Farrah Flegal ◽  
Joan H.M. Knoll ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Estimation Error ◽  
Chromosome Morphology ◽  
Average Dose ◽  
High Quality ◽  
Metaphase Cell ◽  
Three Samples ◽  
Cellular Debris ◽  
Segmentation Methods ◽  
Chromosome Fragments

Accurate digital image analysis of abnormal microscopic structures relies on high quality images and on minimizing the rates of false positive (FP) and negative objects in images. Cytogenetic biodosimetry detects dicentric chromosomes (DCs) that arise from exposure to ionizing radiation, and determines radiation dose received based on DC frequency. Improvements in automated DC recognition increase the accuracy of dose estimates by reclassifying FP DCs as monocentric chromosomes or chromosome fragments. We also present image segmentation methods to rank high quality digital metaphase images and eliminate suboptimal metaphase cells. A set of chromosome morphology segmentation methods selectively filtered out FP DCs arising primarily from sister chromatid separation, chromosome fragmentation, and cellular debris. This reduced FPs by an average of 55% and was highly specific to these abnormal structures (≥97.7%) in three samples. Additional filters selectively removed images with incomplete, highly overlapped, or missing metaphase cells, or with poor overall chromosome morphologies that increased FP rates. Image selection is optimized and FP DCs are minimized by combining multiple feature based segmentation filters and a novel image sorting procedure based on the known distribution of chromosome lengths. Applying the same image segmentation filtering procedures to both calibration and test samples reduced the average dose estimation error from 0.4 Gy to <0.2 Gy, obviating the need to first manually review these images. This reliable and scalable solution enables batch processing for multiple samples of unknown dose, and meets current requirements for triage radiation biodosimetry of high quality metaphase cell preparations.

A Digital Pathology Platform for Artificial Intelligence Data Sharing (Preprint)

2020 ◽  
Author(s):  
Yunsook Kang ◽  
Yoo Jung Kim ◽  
Seongkeun Park ◽  
Gun Ro ◽  
Choyeon Hong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Data Sharing ◽  
Medical Image ◽  
Medical Images ◽  
Digital Pathology ◽  
Image Data ◽  
High Quality ◽  
Web Based ◽  
Search Data ◽  
Tumor Area

BACKGROUND High-quality learning materials are needed for artificial intelligence (AI) development, but are not practically available; this situation is especially poor in the medical field. In particular, annotating medical images (e.g., annotation for tumor area by pathologists) is massive as well as expensive, and subject to privacy protection. These are major limitations for AI developers to approach and reproduce medical image data. OBJECTIVE This study aimed to reduce barriers for AI researchers to access medical image datasets by collating and sharing high-quality medical images with pathologists, and to find applicable ways to apply diagnostic AI assistance to reduce the pathologists’ workload. METHODS Pathology slides of tumors of five organs (liver, colon, prostate, pancreas and biliary tract, and kidney) from histologically confirmed cases were selected for this study. After scanning the slides to obtain whole slide digital images, the patient information was de-identified, and annotation for the tumor area was performed by the pathologist. Next, an AI-assisted annotation process was used in parallel to improve the annotation workload of pathologists and to draw complex lesion boundaries more accurately. This allowed all the data to include the annotations confirmed by experienced pathologists, and to be used as an AI learning dataset. RESULTS A web-based data-sharing platform for AI learning was built, and was unveiled in 2019. In total, 3,100 massive datasets of 5 organ carcinomas were shared through this platform, and were accessible to all researchers. The platform had the advantage that users could search data visually and intuitively; except for commercial purposes, all researchers made free use of the provided dataset for their research. Finally, the platform also provided five image data pre-processing algorithms that could help AI modeling learners. CONCLUSIONS We built and operated a web-based data-sharing platform for AI researchers providing a high-quality digital pathology dataset personally annotated by pathologists. We hope that our experience will help researchers who want to build such a platform in future, by sharing issues gained from collecting and sharing these valuable data.

scholarly journals Generation and Annotation of Simulation-Real Ship Images for Convolutional Neural Networks Training and Testing

2021 ◽  
Vol 11 (13) ◽  
pp. 5931
Author(s):  
Ji’an You ◽  
Zhaozheng Hu ◽  
Chao Peng ◽  
Zhiqiang Wang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Annotation ◽  
Three Dimensional ◽  
Image Data ◽  
Detection Algorithm ◽  
Simulation Software ◽  
High Quality ◽  
Annotation Method ◽  
Detection Of Objects

Large amounts of high-quality image data are the basis and premise of the high accuracy detection of objects in the field of convolutional neural networks (CNN). It is challenging to collect various high-quality ship image data based on the marine environment. A novel method based on CNN is proposed to generate a large number of high-quality ship images to address this. We obtained ship images with different perspectives and different sizes by adjusting the ships’ postures and sizes in three-dimensional (3D) simulation software, then 3D ship data were transformed into 2D ship image according to the principle of pinhole imaging. We selected specific experimental scenes as background images, and the target ships of the 2D ship images were superimposed onto the background images to generate “Simulation–Real” ship images (named SRS images hereafter). Additionally, an image annotation method based on SRS images was designed. Finally, the target detection algorithm based on CNN was used to train and test the generated SRS images. The proposed method is suitable for generating a large number of high-quality ship image samples and annotation data of corresponding ship images quickly to significantly improve the accuracy of ship detection. The annotation method proposed is superior to the annotation methods that label images with the image annotation software of Label-me and Label-img in terms of labeling the SRS images.

Image stabilization technology for active bridge-vessel anti-collision monitoring system

2021 ◽  
Author(s):  
Zhufeng Shao ◽  
Haiying Ma ◽  
Ye Xia ◽  
Junjie Wang
Keyword(s):  
New Technologies ◽  
Target Recognition ◽  
Image Data ◽  
Tracking Data ◽  
Collision System ◽  
High Quality ◽  
Image Stabilization ◽  
Site Monitoring ◽  
New Research ◽  
Sea Area

<p>In recent years, the active anti-collision system using new technologies such as image target recognition between ship and bridge becomes a new research hotspot. Due to camera jitter, it is not easy to deeply mine the monitoring image data. This paper puts forward an anti-jitter algorithm to obtain the ship monitoring track in the sea area removing the camera jitter. It uses electronic image stabilization, sea-sky line anti jitter filtering, and other methods to process the on-site monitoring video, then compares the effect of each technique, and finally obtains high-quality ship tracking data. Through this method, a high-quality ship monitoring track in the bridge area can be obtained.</p>

scholarly journals Integrated Pathology Informatics Enables High-Quality Personalized and Precision Medicine: Digital Pathology and Beyond

2017 ◽  
Vol 142 (3) ◽  
pp. 369-382 ◽  
Author(s):  
Zoya Volynskaya ◽  
Hung Chow ◽  
Andrew Evans ◽  
Alan Wolff ◽  
Cecilia Lagmay-Traya; ◽  
...  
Keyword(s):  
Data Collection ◽  
Precision Medicine ◽  
Critical Role ◽  
Digital Pathology ◽  
Geographic Area ◽  
Whole Slide Imaging ◽  
High Quality ◽  
Pathology Informatics ◽  
Prognosis And Prediction ◽  
The Right

Context.— The critical role of pathology in diagnosis, prognosis, and prediction demands high-quality subspecialty diagnostics that integrates information from multiple laboratories. Objective.— To identify key requirements and to establish a systematic approach to providing high-quality pathology in a health care system that is responsible for services across a large geographic area. Design.— This report focuses on the development of a multisite pathology informatics platform to support high-quality surgical pathology and hematopathology using a sophisticated laboratory information system and whole slide imaging for histology and immunohistochemistry, integrated with ancillary tools, including electron microscopy, flow cytometry, cytogenetics, and molecular diagnostics. Results.— These tools enable patients in numerous geographic locations access to a model of subspecialty pathology that allows reporting of every specimen by the right pathologist at the right time. The use of whole slide imaging for multidisciplinary case conferences enables better communication among members of patient care teams. The system encourages data collection using a discrete data synoptic reporting module, has implemented documentation of quality assurance activities, and allows workload measurement, providing examples of additional benefits that can be gained by this electronic approach to pathology. Conclusion.— This approach builds the foundation for accurate big data collection and high-quality personalized and precision medicine.

High Definition Television (HDTV) and Video Surveillance

2014 ◽  
pp. 219-223
Keyword(s):  
Video Surveillance ◽  
Video Quality ◽  
Image Data ◽  
Good Choice ◽  
Frame Rate ◽  
Surveillance Video ◽  
High Definition ◽  
High Quality ◽  
High Definition Television ◽  
Quality Video

High definition television is becoming ever more popular, opening up the market to new high-definition technologies. Image quality and color fidelity have experienced improvements faster than ever. The video surveillance market has been affected by high definition television demand. Since video surveillance calls for large amounts of image data, high-quality video frame rates are generally compromised. However, a network camera that conforms to high definition television standards shows good performance in high frame rate, resolution, and color fidelity. High quality network cameras are a good choice for surveillance video quality.

Computational Nuclei Segmentation Methods in Digital Pathology: A Survey

2019 ◽  
Vol 28 (1) ◽  
pp. 1-13
Author(s):  
Tomohiro Hayakawa ◽  
V. B. Surya Prasath ◽  
Hiroharu Kawanaka ◽  
Bruce J. Aronow ◽  
Shinji Tsuruoka
Keyword(s):  
Digital Pathology ◽  
Nuclei Segmentation ◽  
Segmentation Methods

scholarly journals Iris Segmentation

2018 ◽  
Vol 7 (2.5) ◽  
pp. 77
Author(s):  
Anis Farihan Mat Raffei ◽  
Rohayanti Hassan ◽  
Shahreen Kasim ◽  
Hishamudin Asmuni ◽  
Asraful Syifaa’ Ahmad ◽  
...  
Keyword(s):  
Iris Recognition ◽  
Image Data ◽  
Recognition System ◽  
Environmental Condition ◽  
Segmentation Method ◽  
Iris Segmentation ◽  
Transform Method ◽  
Circular Hough Transform ◽  
Segmentation Methods

The quality of eye image data become degraded particularly when the image is taken in the non-cooperative acquisition environment such as under visible wavelength illumination. Consequently, this environmental condition may lead to noisy eye images, incorrect localization of limbic and pupillary boundaries and eventually degrade the performance of iris recognition system. Hence, this study has compared several segmentation methods to address the abovementioned issues. The results show that Circular Hough transform method is the best segmentation method with the best overall accuracy, error rate and decidability index that more tolerant to ‘noise’ such as reflection.  

On the Estimation Error in Zero-Inflated Poisson Model for Process Control

2003 ◽  
Vol 10 (02) ◽  
pp. 159-169 ◽  
Author(s):  
B. He ◽  
M. Xie ◽  
T. N. Goh ◽  
P. Ranjan
Keyword(s):  
Sample Size ◽  
Poisson Distribution ◽  
Control Chart ◽  
Average Run Length ◽  
Estimation Error ◽  
Accurate Estimate ◽  
Minimum Size ◽  
High Quality ◽  
Run Length ◽  
Run Length Distribution

The control chart based on a Poisson distribution has often been used to monitor the number of defects in sampling units. However, many false alarms could be observed due to extra zero counts, especially for high-quality processes. Therefore, some alternatives have been developed to alleviate this problem, one of which is the control chart based on the zero-inflated Poisson distribution. This distribution takes into account the extra zeros present in the data, and yield more accurate results than the Poisson distribution. However, implementing a control chart is often based on the assumption that the parameters are either known or an accurate estimate is available. For a high quality process, an accurate estimate may require a very large sample size, which is seldom available. In this paper the effect of estimation error is investigated. An analytical approximation is derived to compute shift detection probability and run length distribution. The study shows that the false alarm rates are higher than the desirable level for smaller values of the sample size. This is further supported by smaller average run length. In general, the quantitative results from this paper can be utilized to select a minimum size of the initial sample for estimating the control limits so that certain average run length requirements are met.

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