Neural Network based Whole Heart Segmentation from 3D CT images

Abstract

The most recent research is showing the importance and suitability of neural networks for medical image processing tasks. Nonetheless, their efficiency in segmentation tasks is greatly dependent on the amount of available training data. To overcome issues of using small datasets, various data augmentation techniques have been developed. In this paper, an approach for the whole heart segmentation based on the convolutional neural network, specifically on the 3D U-Net architecture, is presented. Also, we propose the incorporation of the principal component analysis as an additional data augmentation technique. The network is trained end-to-end, i.e., no pre-trained network is required. Evaluation of the proposed approach is performed on CT images from MICCAI 2017 Multi-Modality Whole Heart Segmentation Challenge dataset, delivering in a three-fold cross-validation an average dice coefficient overlap of 88.2% for the whole heart, i.e. all heart substructures. Final segmentation results show a high accuracy with the ground truth, indicating that the proposed approach is competitive to the state-of-the-art. Additionally, experiments on the influence of different learning rates are provided as well, showing the optimal learning rate of 0.005 to give the best segmentation results.

AI-Assisted CBCT Data Management in Modern Dental Practice: Benefits, Limitations and Innovations

“…Recently, fully convolutional networks (FCNs), most notably the UNet architecture, have greatly improved the accuracy and speed of semantic segmentation tasks, and hence medical segmentation and analysis tasks. The UNet architecture makes heavy use of contextual information [12,13]. The accurate segmentation of anatomical structures plays an important role in many clinical applications including dentistry.…”