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Updated: May 10, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Snake model-based lymphoma segmentation for sequential CT images.

Qiang Chen1, Fang Quan, Jiajing Xu

  • 1Department of Radiology, Stanford University, Stanford, CA, USA. chen2qiang@163.com

Computer Methods and Programs in Biomedicine
|June 22, 2013
PubMed
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This study introduces an automated algorithm for segmenting lymph nodes in CT scans to track cancer treatment effectiveness. The fast and accurate method achieved 100% detection and 95% correct clinical assessment, aiding therapy evaluation.

Area of Science:

  • Medical Imaging
  • Computational Biology
  • Radiology

Background:

  • Accurate measurement of lesion size in follow-up CT scans is crucial for assessing cancer treatment efficacy.
  • Automated methods are needed to improve the efficiency and consistency of lymph node analysis in longitudinal studies.

Purpose of the Study:

  • To develop and evaluate an automatic algorithm for identifying and segmenting lymph nodes in CT images over time.
  • To assess the algorithm's accuracy, speed, and clinical utility in cancer therapy monitoring.

Main Methods:

  • A two-step image registration (coarse and fine) was employed for lymph node localization.
  • Initial segmentation was refined using intensity and edge information, followed by a snake model for precise boundary delineation.
  • The algorithm was validated on 76 test cases from 14 patients, comparing baseline and follow-up CT images.
Keywords:
Image registrationImage segmentationLymphomaSnake modelTemplate matching

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Analysis of Lymph Node Volume by Ultra-High-Frequency Ultrasound Imaging in the Braf/Pten Genetically Engineered Mouse Model of Melanoma

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Last Updated: May 10, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Analysis of Lymph Node Volume by Ultra-High-Frequency Ultrasound Imaging in the Braf/Pten Genetically Engineered Mouse Model of Melanoma
08:18

Analysis of Lymph Node Volume by Ultra-High-Frequency Ultrasound Imaging in the Braf/Pten Genetically Engineered Mouse Model of Melanoma

Published on: September 8, 2021

Main Results:

  • Achieved 100% successful detection of lymph nodes across 76 test cases.
  • Demonstrated 95% correct clinical assessment based on Response Evaluation Criteria in Solid Tumors (RECIST).
  • Quantitative evaluation showed good results with metrics like average Hausdorff distance, and the algorithm processed cases in an average of 2.58 seconds.

Conclusions:

  • The proposed algorithm offers fast and highly accurate lymph node segmentation in CT images.
  • This automated approach can significantly aid in the tracking and evaluation of cancer therapy.
  • The method shows potential for routine clinical application in oncology patient management.