Paper Info
Title
Developing an explainable deep learning boundary correction method by incorporating cascaded x-Dim models to improve segmentation defects in liver CT images
Abstract
Deep learning methods achieved remarkable results in medical image analysis tasks but it has not yet been widely used by medical professionals. One of the main reasons for this restricted usage is the uncertainty of the reasons that influence the decision of the model. Explainable AI methods have been developed to improve the transparency, interpretability, and explainability of the black-box AI methods. The result of an explainable segmentation method will be more trusted by experts. In this study, we designed an explainable deep correction method by incorporating cascaded 1D and 2D models to refine the output of other models and provide reliable yet accurate results. We implemented a 2-step loop with a 1D local boundary validation model in the first step, and a 2D image patch segmentation model in the second step, to refine incorrect segmented regions slice-by-slice. The proposed method improved the result of the CNN segmentation models and achieved state-of-the-art results on 3D liver segmentation with the average dice coefficient of 98.27 on the Sliver07 dataset.
Year
DOI
Venue
2022
10.1016/j.compbiomed.2021.105106
COMPUTERS IN BIOLOGY AND MEDICINE
Keywords
DocType
Volume
Medical image analysis, Liver segmentation, Convolutional neural networks, Explainable deep learning
Journal
140
ISSN
Citations 
PageRank 
0010-4825
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Saeed Mohagheghi100.34
Amir Hossein Foruzan200.68