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Intravital Microscopy of the Inguinal Lymph Node
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Lymph node segmentation by dynamic programming and active contours.

Yongqiang Tan1, Lin Lu2, Apurva Bonde3

  • 1Google Inc, Los Angeles, CA, 90291, USA.

Medical Physics
|March 4, 2018
PubMed
Summary
This summary is machine-generated.

A new 3D algorithm accurately segments enlarged lymph nodes for cancer staging. This automated method shows potential clinical value in quantifying lymph node size changes during treatment.

Keywords:
active contourscomputed tomography (CT)dynamic programminglymph node segmentationsphere subdivision

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Area of Science:

  • Medical imaging
  • Computational anatomy
  • Oncology

Background:

  • Enlarged lymph nodes are critical for cancer staging and monitoring treatment response.
  • Accurate segmentation of lymph nodes is challenging due to unclear boundaries and complex surrounding anatomy.

Purpose of the Study:

  • To introduce a novel three-dimensional (3D) algorithm for the automated segmentation of enlarged lymph nodes.
  • To address the challenges in lymph node segmentation for improved cancer staging and treatment evaluation.

Main Methods:

  • The algorithm utilizes a user-defined region of interest (ROI) to initialize a triangle mesh.
  • Dynamic programming determines ray intersections to form the mesh, which initializes an active contour.
  • The active contour evolves to a low-energy boundary, segmenting the lymph node.

Main Results:

  • The algorithm achieved a mean Dice coefficient of 83.2% compared to the majority vote of expert segmentations.
  • Radiologist inter-observer agreement (Dice coefficients) ranged from 84.6% to 88.3%.

Conclusions:

  • The developed 3D segmentation algorithm demonstrates promising performance.
  • This technique holds potential clinical value for precise quantification of enlarged lymph nodes in cancer patients.