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14 August 2019 Convolutional-neural-network-based feature extraction for liver segmentation from CT images
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Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 1117934 (2019) https://doi.org/10.1117/12.2540175
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China
Abstract
Over the last few years, major breakthroughs were achieved in the application of deep learning in many computer vision tasks, such as image classification and segmentation. The automatic liver segmentation from CT images has become an important area in clinical research, including radiotherapy, liver volume measurement, and liver transplant surgery. This paper proposes a novel convolutional neural network for liver segmentation (CNN-LivSeg) algorithm that involves three convolutional (each convolutional layer followed by max-pooling layer) and two fully connected layers with a final 2- way softmax is used for liver discrimination. The weight initialization is based on a random Gaussian, which performed a distance preserving-embedding of the data. To avoid using the fully 3D CNN network which is computationally expensive and time-consuming, 2D patches were extracted and processed for segmentation. Experiments were performed on MICCAI-SLiver07 as a benchmark dataset. The mean ratios of Dice similarity coefficient, Jaccard similarity index, accuracy, specificity, and sensitivity were 0.9541, 0.9122, 0.9725, 0.9904, and 0.9652, respectively, thereby suggesting that the proposed method performed well on the test images.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mubashir Ahmad, Yuan Ding, Syed Furqan Qadri, and Jian Yang "Convolutional-neural-network-based feature extraction for liver segmentation from CT images", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117934 (14 August 2019); https://doi.org/10.1117/12.2540175
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