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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Related Experiment Video

Updated: Dec 10, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

A New Method for CTC Images Recognition Based on Machine Learning.

Binsheng He¹, Qingqing Lu^2,3, Jidong Lang^2,3

¹Academician Workstation, Changsha Medical University, Changsha, China.

Frontiers in Bioengineering and Biotechnology

|August 28, 2020

Summary

Machine learning automates circulating tumor cell (CTC) counting, improving accuracy. This AI approach enhances tumor prognosis and treatment monitoring by reducing manual errors in CTC identification.

Keywords:

CNN network circulating tumor cells (CTCs)imFISH image segmentation machine learning

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Published on: October 10, 2018

Area of Science:

Oncology
Biomedical Engineering
Computational Biology

Background:

Circulating tumor cells (CTCs) are crucial biomarkers for tumor prognosis, treatment monitoring, and recurrence detection.
Current CTC counting relies on manual methods, which are labor-intensive and prone to human error.
Automated medical image recognition using machine learning offers a solution to improve efficiency and accuracy.

Purpose of the Study:

To develop and evaluate a machine learning model for automated identification and counting of CTCs.
To reduce the workload and potential for misjudgment associated with manual CTC analysis.
To enhance the automation level in CTC detection for improved clinical utility.

Main Methods:

Collected CTC test results from 600 patients with immunofluorescence staining.
Applied image processing techniques including denoising, filtering, edge detection, and morphological operations using OpenCV.
Utilized convolutional neural network (CNN) deep learning models for training and testing on 2300 segmented cell images.

Main Results:

The machine learning model achieved a sensitivity of 90.3% and a specificity of 91.3% in identifying CTCs.
The study successfully segmented CTC images using a combination of traditional and machine learning approaches.
The model demonstrated high accuracy in distinguishing positive CTC nuclei from negative controls.

Conclusions:

Machine learning, specifically CNNs, can effectively automate CTC identification, offering a promising alternative to manual counting.
The developed model shows significant potential for improving the accuracy and efficiency of CTC analysis in clinical settings.
Further model refinement is planned to achieve even higher sensitivity and specificity for enhanced diagnostic capabilities.