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Convolutional Neural Network for Segmentation and Measurement of Intima Media Thickness.

Sudha S¹, Jayanthi K B², Rajasekaran C²

¹Department of Electronics and Communication Engineering, K.S.Rangasamy College of Technology, Tamil Nadu, India. sudhasubramaniam@gmail.com.

Journal of Medical Systems

|July 11, 2018

Summary

A new deep learning method accurately measures Carotid Intima Media Thickness (IMT), a key cardiovascular disease risk marker. This novel approach uses Convolutional Neural Networks (CNNs) for precise IMT identification and measurement.

Keywords:

Cardio vascular disease (CVD)Carotid intima media thickness (CIMT)Convolutional neural network (CNN)Deep learning

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Area of Science:

Medical Imaging
Artificial Intelligence
Cardiovascular Diagnostics

Background:

Carotid Intima Media Thickness (IMT) is a crucial indicator for assessing cardiovascular disease (CVD) risk.
Accurate measurement of IMT is essential for early detection and management of atherosclerosis.
Current IMT measurement techniques can be subjective and time-consuming.

Purpose of the Study:

To introduce a novel deep Convolutional Neural Network (CNN) based method for the automated identification and measurement of IMT.
To evaluate the efficacy of the proposed CNN method in measuring IMT on the far wall of the Common Carotid Artery (CCA).
To establish a new, potentially more accurate and efficient approach for CVD risk stratification using IMT.

Main Methods:

Utilized an 8-layer deep CNN architecture for Region of Interest (ROI) extraction.
Employed patch-based segmentation with 2640 patches for training the network in ROI localization.
Applied binary thresholding combined with a snake algorithm to delineate lumen-intima and media-adventitia boundaries for IMT measurement within the defined ROI.
Collected ultrasound recordings from 110 subjects, including Right Common Carotid Artery (RCCA) and Left Common Carotid Artery (LCCA) frames (220 total).

Main Results:

The CNN method successfully identified and localized the Region of Interest (ROI) for IMT measurement.
The segmentation and boundary detection algorithms accurately isolated the Intima Media Complex (IMC).
Mean difference in IMT measurements for 20 cases was found to be 0.08 mm, indicating high precision.

Conclusions:

The proposed deep CNN method offers a novel and effective approach for automated IMT measurement.
This technique demonstrates high accuracy and precision, comparable to established methods.
The automated IMT measurement holds significant potential for improving cardiovascular disease risk assessment and patient management.