Robust Medical Instrument Segmentation Challenge 2019.
Tobias RoßAnnika ReinkePeter M. FullMartin WagnerHannes KenngottMartin ApitzHellena HempeDiana Mindroc FilimonPatrick ScholzThuy Nuong TranPierangela BrunoPablo ArbeláezGui-Bin BianSebastian BodenstedtJon Lindström BolmgrenLaura Bravo SánchezHua-Bin ChenCristina GonzálezDong GuoPål HalvorsenPheng-Ann HengEnes HosgorZeng-Guang HouFabian IsenseeDebesh JhaTingting JiangYueming JinKadir KirtaçSabrina KletzStefan LegerZhixuan LiKlaus H. Maier-HeinZhen-Liang NiMichael A. RieglerKlaus SchoeffmannRuohua ShiStefanie SpeidelMichael StenzelIsabell TwickGuotai WangJiacheng WangLiansheng WangLu WangYujie ZhangYan-Jie ZhouLei ZhuManuel WiesenfarthAnnette Kopp-SchneiderBeat Peter Müller-StichLena Maier-HeinPublished in: CoRR (2020)
Keyphrases
- level set
- medical imaging
- segmentation algorithm
- image segmentation
- robust segmentation
- fully automatic
- segmentation accuracy
- segmentation method
- medical images
- multiscale
- outlier rejection
- computationally efficient
- energy function
- neural network
- region growing
- object segmentation
- image analysis
- input image
- medical data
- robust estimation
- grey level
- fully unsupervised
- microscopy images
- optimal segmentation