Abstract
Liver segmentation is a crucial step in computer-assisted diagnosis and surgical planning of liver diseases. However, it is still a quite challenging task due to four reasons. First, the grayscale of the liver and its adjacent organ tissues is similar. Second, partial volume effect makes the liver contour blurred. Third, most clinical images have serious pathology such as liver tumor. Forth, each person’s liver shape is discrepant. In this paper, we proposed DSL (detection and segmentation laboratory) method based on Faster R-CNN (faster regions with CNN features) and DeepLab. The DSL consists of two steps: to reduce the scope of subsequent liver segmentation, Faster R-CNN is employed to detect liver area. Next, the detection results are input to DeepLab for segmentation. This work is evaluated on two datasets: 3Dircadb and MICCAI-Sliver07. Compared with the state-of-the-art automatic methods, our approach has achieved better performance in terms of VOE, RVD, ASD and total score.
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Tang, W., Zou, D., Yang, S., Shi, J. (2018). DSL: Automatic Liver Segmentation with Faster R-CNN and DeepLab. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11140. Springer, Cham. https://doi.org/10.1007/978-3-030-01421-6_14
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