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Related Experiment Video

Updated: Sep 26, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Phase recognition in contrast-enhanced CT scans based on deep learning and random sampling.

Binh T Dao¹, Thang V Nguyen¹, Hieu H Pham^1,2,3

¹Smart Health Center, VinBigData JSC, Hanoi, Vietnam.

Medical Physics

|April 16, 2022

Summary

This study introduces a fast, accurate computed tomography (CT) phase classification method using deep convolutional neural networks (CNNs) and random sampling. The novel approach significantly reduces latency compared to existing 3D methods for abdominal CT analysis.

Keywords:

CT scans deep learning phase recognition

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

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Area of Science:

Medical Imaging
Artificial Intelligence in Radiology
Machine Learning for Healthcare

Background:

Accurate classification of contrast phases in abdominal CT scans is crucial for automated interpretation systems.
Current 3D CNN methods for CT phase classification are computationally intensive and slow.

Purpose of the Study:

To develop and validate a precise and fast multiphase classifier for abdominal CT scans.
To recognize three main contrast phases: noncontrast, arterial, and venous.

Main Methods:

A novel method combining deep CNNs with a random sampling mechanism for phase recognition.
CNNs perform slicewise phase prediction, with random sampling selecting input slices.
Majority voting of CNN predictions synthesizes results for final scan-level classification.

Main Results:

Achieved a mean F1 score of 92.09% on an internal test set (358 scans).
Demonstrated high accuracy on external datasets (CTPAC-CCRCC: 76.79%, LiTS: 86.94%).
Outperformed state-of-the-art 3D approaches with reduced inference time.

Conclusions:

The proposed 2D deep learning approach with random sampling offers superior accuracy and reduced latency compared to existing methods.
This precise, fast multiphase classifier is valuable for analyzing real-world abdominal CT data.