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Related Concept Videos

Quality Control01:05

Quality Control

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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
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Quality Assurance01:19

Quality Assurance

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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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Quality of Water01:19

Quality of Water

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In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
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Pulse amplitude and quality01:17

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Pulse amplitude is a crucial indicator of cardiac health because it provides valuable insights into the strength of left ventricular contractions and the overall uniformity of blood circulation within the vasculature. The strength of the pulse is directly related to the force with which the heart contracts and the volume of blood being pumped.
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Testing Water Quality01:14

Testing Water Quality

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When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
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ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
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Image Quality and Segmentation.

Gargi V Pednekar1, Jayaram K Udupa2, David J McLaughlin1

  • 1Quantitative Radiology Solutions, 3624 Market St., Suite 5E, Philadelphia PA 19104 United States.

Proceedings of Spie--The International Society for Optical Engineering
|August 17, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for evaluating image segmentation quality by developing algorithms that generate object and image quality scores. These scores are derived from reader-assigned grades on key quality criteria, addressing a gap in current literature for objective segmentation assessment.

Keywords:
Image qualityimage segmentationsegmentation evaluation

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Radiology

Background:

  • Image segmentation algorithms' performance is affected by image quality and object appearance.
  • A standardized method for evaluating segmentation quality based on these factors is lacking in existing research.

Purpose of the Study:

  • To develop and validate a quantitative method for assessing image segmentation quality.
  • To establish object and image quality scores (OQS and IQS) that correlate with segmentation performance.

Main Methods:

  • Defined key segmentation quality criteria: posture deviations, image noise, beam hardening artifacts, shape distortion, pathology, intensity deviation, and contrast.
  • Utilized trained reader grading for each criterion per object and image.
  • Developed algorithms based on logical predicates to compute numeric OQS and IQS (1-10 scale).
  • Applied Automatic Anatomy Recognition (AAR) framework for object recognition and delineation accuracy analysis.

Main Results:

  • Established a quantitative scoring system for object and image quality relevant to segmentation.
  • Analyzed OQS and IQS in relation to segmentation accuracy across diverse imaging studies.
  • Demonstrated the method's applicability on 216 head & neck and 211 thoracic CT scans.

Conclusions:

  • The proposed method provides a robust framework for evaluating image segmentation quality.
  • Objective quality scores can enhance the reliability and interpretability of automated segmentation results.
  • This approach is crucial for advancing the clinical application of image analysis algorithms.