3D Domain Adaptive Instance Segmentation via Cyclic Segmentation GANs-Reference-Cited by-同舟云学术

3D Domain Adaptive Instance Segmentation via Cyclic Segmentation GANs

Published:2023-08 Issue:8 Volume:27 Page:4018-4027
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Lauenburg Leander¹^ORCID,Lin Zudi¹^ORCID,Zhang Ruihan²^ORCID,Santos Márcia dos³,Huang Siyu¹,Arganda-Carreras Ignacio⁴^ORCID,Boyden Edward S.²,Pfister Hanspeter¹^ORCID,Wei Donglai⁵

Affiliation:

1. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA, USA

2. Media Lab, MIT, Cambridge, MA, USA

3. Harvard University, Allston, MA, USA

4. Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia-San Sebastian, Spain

5. Department of Computer Science, Boston College, Chestnut Hill, MA, USA

Funder

IFI Program of the German Academic Exchange Service

University of the Basque Country UPV/EHU

Ministerio de Ciencia, Innovación y Universidades, AEI

NIH

Lisa Yang, in part by John Doerr

Schmidt Futures

N S F

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

https://ieeexplore.ieee.org/ielam/6221020/10210140/10138543-aam.pdf

Reference33 articles.

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2. Segmentation-enhanced cyclegan;januszewski,2019

3. Superhuman accuracy on the snemi3D connectomics challenge;lee,2017

4. SUSAN: Segment unannotated image structure using adversarial network;liu;Magn Reson Med,2019

5. Pytorch connectomics: A scalable and flexible segmentation framework for em connectomics;lin,2021

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