@inproceedings{e7b844ba12684b749fa0f6c8d55147b7,
title = "Deep-stacked auto encoder for liver segmentation",
abstract = "Deep learning methods have been successfully applied to feature learning in medical applications. In this paper, we proposed a Deep Stacked Auto-Encoder (DSAE) for liver segmentation from CT images. The proposed method composes of three major steps. First, we learned the features with unlabeled data using the auto encoder. Second, these features are fine-tuned to classify the liver among other abdominal organs. Using this technique we got promising classification results on 2D CT data. This classification of the data helps to segment the liver from the abdomen. Finally, segmentation of a liver is refined by post processing method. We focused on the high accuracy of the classification task because of its effect on the accuracy of a better segmentation. We trained the deep stacked auto encoder (DSAE) on 2D CT images and experimentally shows that this method has high classification accuracy and can speed up the clinical task to segment the liver. The mean DICE coefficient is noted to be 90.1% which is better than the state of art methods.",
keywords = "Classification, Deep learning, Liver, Segmentation",
author = "Mubashir Ahmad and Jian Yang and Danni Ai and Qadri, {Syed Furqan} and Yongtian Wang",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2018.; 12th Chinese conference on Advances in Image and Graphics Technologies, IGTA 2017 ; Conference date: 30-06-2017 Through 01-07-2017",
year = "2018",
doi = "10.1007/978-981-10-7389-2_24",
language = "English",
isbn = "9789811073885",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "243--251",
editor = "Xiaoru Yuan and Duh, {Henry Been-Lirn} and Yongtian Wang and Yue Liu and Jian Yang and Shengjin Wang and Ran He",
booktitle = "Advances in Image and Graphics Technologies - 12th Chinese conference, IGTA 2017, Revised Selected Papers",
address = "Germany",
}