Background:
Image evaluation of scar tissue plays a significant role in the diagnosis of
cardiovascular diseases. Segmentation of the scar tissue is the first step towards evaluating the
morphology of the scar tissue. Then, with the use of CT images, the deep learning approach can
be applied to identify possible scar tissue in the left ventricular endocardial wall.
Objective:
To develop an automated method for detecting the endocardial scar tissue in the left
ventricular using Deep learning approach.
Method:
Pixel values of the endocardial wall for each image in the sequence were extracted.
Morphological operations, including defining regions of the endocardial wall of the LV where
scar tissue could predominate, were performed. Convolutional Neural Networks (CNN) is a deep
learning application, which allowed choosing appropriate features from delayed enhancement cardiac
CT images to distinguish between endocardial scar and healthy tissues of the LV by applying
pixel value-based concepts.
Result:
We achieved 89.23% accuracy, 91.11% sensitivity, and 87.75% specificity in the detection
of endocardial scars using the CNN-based method.
Conclusion:
Our findings reveal that the CNN-based method yielded robust accuracies in LV endocardial
scar detection, which is currently the most extensively used pixel-based method of deep
learning. This study provides a new direction for the assessment of scar tissue in imaging modalities
and provides a potential avenue for clinical adaptations of these algorithms. Additionally this
methodology, in comparison with those in the literature, provides specific advantages in its translational
ability to clinical use.
FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images
