@inproceedings{c4691f1ab4324279bebb6ad81fec9441,
title = "Deep-FExt: Deep feature extraction for vessel segmentation and centerline prediction",
abstract = "Feature extraction is a very crucial task in image and pixel (voxel) classification and regression in biomedical image modelling. In this work we present a feature extraction scheme based on inception models for pixel classification tasks. We extract features under multi-scale and multi-layer schemes through convolutional operators. Layers of Fully Convolutional Network are later stacked on these feature extraction layers and trained end-to-end for the purpose of classification. We test our model on the DRIVE and STARE public data sets for the purpose of segmentation and centerline detection and it outperforms most existing hand crafted or deterministic feature schemes found in literature. We achieve an average maximum Dice of 0.85 on the DRIVE data set which outperforms the scores from the second human annotator of this data set. We also achieve an average maximum Dice of 0.85 and kappa of 0.84 on the STARE data set. Even though these datasets are only 2-D we also propose ways of extending this feature extraction scheme to handle 3-D datasets.",
keywords = "Biomedical image modelling, Centerline prediction, Convolutional Networks, Feature extraction, Image and pixel classification and regression, Inception models, Vessel segmentation",
author = "Giles Tetteh and Markus Rempfler and Claus Zimmer and Menze, \{Bjoern H.\}",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 ; Conference date: 10-09-2017 Through 10-09-2017",
year = "2017",
doi = "10.1007/978-3-319-67389-9\_40",
language = "English",
isbn = "9783319673882",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "344--352",
editor = "Yinghuan Shi and Heung-Il Suk and Kenji Suzuki and Qian Wang",
booktitle = "Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings",
}