Segmentation

Here are some slides I made to present this CVPR 2019 paper in our reading group:

In this paper, the authors proposed a fully convolutional neural network architecture for biomedical image segmentation which overcame the limitations of the contemporary algorithms. Unlike other popular algorithms then, the proposed network did not suffer from the redundancy arising out of overlapping training patches. Moreover, the authors eliminate the trade-off between localization accuracy and the use of context and state that “good localization and the use of context are possible at the same time”. ...

Drawing inspiration from the popular VGG networks, the paper proposes using a deep convolutional neural network architecture with small convolutional kernels for segmentation of gliomas in MRI images. The authors discuss the relative advantages of using small kernels, and also explore the use of intensity normalization as a pre-processing step, which was unconventional in CNN-based segmentation methods. The proposed algorithm obtained the first position for the complete, the core, and the enhancing regions in Dice Similarity Coefficient metric in the Brain Tumor Segmentation Challenge 2013 database (BraTS 2013). ...

This paper proposes an end-to-end trained fully convolutional neural network model to process 3D image volumes. Unlike previous works that processed the input volumes slice-wise or patch-wise, the authors propose to use volumetric convolutions. Moreover, a new objective function formulated using the Dice coefficient is proposed to be optimized, and the authors demonstrate the fast and superior performance of the algorithm on the segmentation of prostate MRI volumes. ...