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Related Concept Videos

Classification of Leukocytes01:30

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
Detailed Structure and Function of Lymph Nodes01:23

Detailed Structure and Function of Lymph Nodes

Lymph nodes are bean-shaped structures that cluster along the lymphatic vessels in the inguinal, axillary, and cervical regions. Each node is divided into compartments by a capsule that extends trabeculae inward.
From a histological perspective, lymph nodes can be split into two main areas: the superficial cortex and the deep medulla. The outer cortex is populated by dendritic cells, macrophages, and B lymphocytes, which are densely packed into follicles. When these B-lymphocytes are presented...

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Related Experiment Video

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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
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2D view aggregation for lymph node detection using a shallow hierarchy of linear classifiers.

Ari Seff, Le Lu, Kevin M Cherry

    Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
    |October 22, 2014
    PubMed
    Summary
    This summary is machine-generated.

    A new algorithm detects enlarged lymph nodes (LNs) by breaking down the 3D problem into 2D tasks on CT scans. This method improves cancer diagnosis and treatment monitoring through automated lymph node detection.

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    Area of Science:

    • Medical Imaging
    • Computer-Aided Diagnosis
    • Oncology

    Background:

    • Enlarged lymph nodes (LNs) are crucial indicators in cancer diagnosis, staging, and treatment response assessment.
    • Automated detection of LNs in medical imaging is a significant challenge due to data complexity.

    Purpose of the Study:

    • To develop and validate a novel algorithm for automated 3D lymph node detection in CT images.
    • To improve the accuracy and efficiency of lymph node detection for clinical applications.

    Main Methods:

    • The proposed algorithm decomposes 3D lymph node detection into 2D object detection subtasks on sampled CT slices, addressing the curse of dimensionality.
    • Utilizes Histogram of Oriented Gradients (HOG) features for 2D detection within a 45x45 voxel field-of-view.
    • Employs max-pooling and sparse linear fusion to aggregate 2D detection scores for robust 3D lymph node identification.

    Main Results:

    • The algorithm achieved 78.0% sensitivity at 6 false positives/volume (FP/vol.) for mediastinal LNs and 73.1% sensitivity at 6 FP/vol. for abdominal LNs.
    • Performance on validation datasets (90 patients, 389 mediastinal LNs; 86 patients, 595 abdominal LNs) showed high accuracy.
    • Results favorably compare with existing state-of-the-art methods in automated lymph node detection.

    Conclusions:

    • The proposed 2D decomposition approach offers a tractable and robust method for 3D lymph node detection.
    • This algorithm holds promise for enhancing cancer diagnosis, staging, and treatment monitoring through improved automated detection.