TreeNet: Multi-loss Deep Learning Network to Predict Branch Direction for Extracting 3D Anatomical Trees

Mengliu Zhao, Ghassan Hamarneh. TreeNet: Multi-loss Deep Learning Network to Predict Branch Direction for Extracting 3D Anatomical Trees. In Danail Stoyanov, Zeike Taylor, Gustavo Carneiro, Tanveer F. Syeda-Mahmood, Anne L. Martel, Lena Maier-Hein, João Manuel R. S. Tavares, Andrew P. Bradley, João Paulo Papa, Vasileios Belagiannis, Jacinto C. Nascimento, Zhi Lu, Sailesh Conjeti, Mehdi Moradi, Hayit Greenspan, Anant Madabhushi, editors, Deep Learning in Medical Image Analysis - and - Multimodal Learning for Clinical Decision Support - 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings. Volume 11045 of Lecture Notes in Computer Science, pages 47-55, Springer, 2018. [doi]

@inproceedings{ZhaoH18-4,
  title = {TreeNet: Multi-loss Deep Learning Network to Predict Branch Direction for Extracting 3D Anatomical Trees},
  author = {Mengliu Zhao and Ghassan Hamarneh},
  year = {2018},
  doi = {10.1007/978-3-030-00889-5_6},
  url = {https://doi.org/10.1007/978-3-030-00889-5_6},
  researchr = {https://researchr.org/publication/ZhaoH18-4},
  cites = {0},
  citedby = {0},
  pages = {47-55},
  booktitle = {Deep Learning in Medical Image Analysis - and - Multimodal Learning for Clinical Decision Support - 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings},
  editor = {Danail Stoyanov and Zeike Taylor and Gustavo Carneiro and Tanveer F. Syeda-Mahmood and Anne L. Martel and Lena Maier-Hein and João Manuel R. S. Tavares and Andrew P. Bradley and João Paulo Papa and Vasileios Belagiannis and Jacinto C. Nascimento and Zhi Lu and Sailesh Conjeti and Mehdi Moradi and Hayit Greenspan and Anant Madabhushi},
  volume = {11045},
  series = {Lecture Notes in Computer Science},
  publisher = {Springer},
  isbn = {978-3-030-00889-5},
}