Abstract
Deep learning methods are showing progressive development in the medical imaging field. The accuracy of segmentation is improving challengingly with various deep techniques. Cardiac imaging is a very useful modality to evaluate functional analysis of the right ventricle. We propose an integrated model with the learning approach and Active Contour Method in this paper. We have customized UNet architecture on the basis of performance parameters such as dice metric and Haussdorff distance, training accuracy and testing accuracy etc. with MICCAI 2012 RVSC data. Six layer (6L)-UNet architecture was selected for learning the model. The predicted results of UNet are given to active contour model. These are considered as seed points to extract RV contours. Thus, our model replaces the semi-automatic active contour method into fully automatic segmentation method. It shows promising average results of dice metric and Haussdorff distance such as 0.9 and 2 mm. The active contour method is significant to remove undesired surroundings from extracted ROI.
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Abhijit Yadav, A., Ganorkar, S.R. (2023). Improving Right Ventricle Contouring in Cardiac MR Images Using Integrated Approach for Small Datasets. In: Pandit, M., Gaur, M.K., Kumar, S. (eds) Artificial Intelligence and Sustainable Computing. ICSISCET 2022. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-1431-9_19
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