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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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Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes.

Rachel Lea Draelos¹, David Dov², Maciej A Mazurowski³

¹Computer Science Department, Duke University, LSRC Building D101, 308 Research Drive, Duke Box 90129, Durham, North Carolina 27708-0129, United States of America; School of Medicine, Duke University, DUMC 3710, Durham, North Carolina 27710, United States of America.

Medical Image Analysis

|October 31, 2020

Summary

This summary is machine-generated.

This study presents the largest annotated medical imaging dataset for chest CT scans, enabling better machine learning models in radiology. Automated labeling and a deep learning model achieved high accuracy in detecting abnormalities.

Keywords:

chest computed tomography convolutional neural network deep learning machine learning multilabel classification

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Radiology
Medical Imaging
Machine Learning
Artificial Intelligence

Background:

Machine learning in radiology requires large, high-quality labeled datasets for abnormality detection.
Existing datasets are often limited in size and annotation scope.
Chest computed tomography (CT) data is crucial for diagnosing a wide range of thoracic conditions.

Purpose of the Study:

To create the largest multiply-annotated volumetric medical imaging dataset for chest CT.
To develop and validate automated methods for extracting abnormality labels from radiology reports.
To train and evaluate a deep convolutional neural network (CNN) for multi-organ, multi-disease classification of chest CT volumes.

Main Methods:

Curated and analyzed a chest CT dataset comprising 36,316 volumes from 19,993 patients.
Developed a rule-based method for automated extraction of abnormality labels from free-text radiology reports, achieving an average F-score of 0.976.
Trained a deep convolutional neural network (CNN) for classification of 83 abnormalities in chest CT volumes.

Main Results:

The automated labeling method demonstrated high accuracy (average F-score 0.976).
The CNN model achieved an area under the receiver operating characteristic curve (AUROC) >0.90 for 18 abnormalities and an average AUROC of 0.773 for all 83 abnormalities.
Increasing the number of training labels significantly improved model performance, with a 10% AUROC increase for a subset of 9 labels when trained on all 83.

Conclusions:

The developed dataset and automated labeling methods represent a significant advancement for machine learning in radiology.
The study demonstrates the feasibility of training effective deep learning models on large-scale, unfiltered volumetric CT data.
Publicly releasing the code and dataset will facilitate further research and development in AI-driven medical imaging analysis.