A Novel Method for Classification of Multi-phase Abdominal CT Scans Using Deep Learning and Random Sampling

This repository contains the training code for our paper entitled "A Novel Method for Classification of Multi-phase Abdominal CT Scans Using Deep Learning and Random Sampling", which was submitted and under review by Medical Imaging with Deep Learning 2021 (MIDL2021).

Abstract

(CT) scans with multiple phases of contrast enhancement requires an accurate classification of the phases. We propose in this paper a novel method that introduces a random sampling mechanism on top of a convolutional neural network (CNN) for the phase recognition of abdominal CT scans of 3 different phases: non-contrast, arterial, and venous. The CNN works as a slice-wise phase prediction, while the random sampling synthesizes the results over a random subset of the slices. The slice-level classifier is trained on 217,876 slices from 682 CT scans, yielding a mean F1 score of 90.60% on 79,250 slices of the test set. By majority voting on just 20 slices randomly chosen from each scan, the proposed method achieves a mean F1 score of 93.54% on 282 samples of the test set.

Preprocess DICOM image

Image read from raw .dcm file needs to be processed as followed:

• Convert pixel values to HU standards using formula: newValue = RescaleSlope * pixelValue + RescaleIntercept

RescaleSlope and RescaleIntercept can be extracted from the metadata .dcm file

• Apply HU window to the image with window_width=400, window_center=50

Architecture

Model training

1. Data Preparation:

• We preprocess data as mentioned in Preprocess DICOM image section
• Training, validating and testing .csv file of 2D dataset should follow this format:

Study_ID	Image	SeriesNumber	Label
Study id	Path to image	Extract from metadata	slice label

• Training, validating and testing .csv file of 3D dataset should follow this format:

Study_ID	Image	Label
Study id	Path to image	scan label

2. Training Configuration:

Configuration used in the paper are in folder core/config It is recommended that you change training configuration in .yaml files

Command to train the 2D model:

python main.py --config "PATH_TO_CONFIG_FILE"

Command to evaluate the 2D model:

python main.py --config "PATH_TO_CONFIG_FILE" --load "PATH_TO_MODEL_CHECKPOINT" --mode "VALID_OR_TEST"

Command to train the 3D model:

python main3d.py --config "PATH_TO_CONFIG_FILE"

Command to evaluate the 3D model:

python main3d.py --config "PATH_TO_CONFIG_FILE" --load "PATH_TO_MODEL_CHECKPOINT" --mode "VALID_OR_TEST"