Deep Learning models such as Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in 2D medical image analysis. In clinical practice; however, most analyzed and acquired medical data are formed of 3D volumes. In this paper, we present a fast and efficient 3D lung segmentation method based on V-net: a purely volumetric fully CNN. Our model is trained on chest CT images through volume to volume learning, which palliates overfitting problem on limited number of annotated training data. Adopting a pre-processing step and training an objective function based on Dice coefficient addresses the imbalance between the number of lung voxels against that of background. We have leveraged Vnet model by using batch normalization for training which enables us to use higher learning rate and accelerates the training of the model. To address the inadequacy of training data and obtain better robustness, we augment the data applying random linear and non-linear transformations. Experimental results on two challenging medical image data show that our proposed method achieved competitive result with a much faster speed.
Mohammadreza Negahdar, David Beymer, and Tanveer Syeda-Mahmood, "Automated volumetric lung segmentation of thoracic CT images using fully convolutional neural network," Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751J (Presented at SPIE Medical Imaging: February 15, 2018; Published: 27 February 2018); https://doi.org/10.1117/12.2293723.
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