Research Area:  Machine Learning

Abstract:

Lung CT image segmentation is a necessary initial step for lung image analysis, it is a prerequisite step to provide an accurate lung CT image analysis such as lung cancer detection. In this work, we propose a lung CT image segmentation using the U-net architecture, one of the most used architectures in deep learning for image segmentation. The architecture consists of a contracting path to extract high-level information and a symmetric expanding path that recovers the information needed. This network can be trained end-to-end from very few images and outperforms many methods. Experimental results show an accurate segmentation with 0.9502 Dice-Coefficient index.

Keywords:  
Lung Ct
Image Segmentation
Deep Neural Networks
Machine Learning
Deep Learning

Author(s) Name:  Brahim Ait Skourt, Abdelhamid El Hassani, Aicha Majda

Publisher name:  Elsevier

DOI:  10.1016/j.procs.2018.01.104

Volume Information:  Volume 127, 2018, Pages 109-113

Paper Link:   https://www.sciencedirect.com/science/article/pii/S1877050918301157