Table 10.

Brief summary of sixteen published work using LiTS for the development of novel segmentation methods in medical imaging. While many of them focus on methodological contribution, they also advance the state-of-the-art in liver and liver tumor segmentation.

Source	Authors	Key features
MIA	Zhou et al. (2021a)	multimodal registration, unsupervised segmentation, image-guided intervention
MIA	Wang et al. (2021)	conjugate fully convolutional network, pairwise segmentation, proxy supervision
MIA	Zhou et al. (2021b)	3D Deep learning, self-supervised learning, transfer learning
MICCAI	Shirokikh et al. (2020)	loss reweighting, lesion detection
MICCAI	Haghighi et al. (2020)	self-supervised learning, transfer learning, 3D model pre-training
MICCAI	Huang et al. (2020)	co-training of sparse datasets, multi-organ segmentation
MICCAI	Wang et al. (2019)	volumetric attention, 3D segmentation
MICCAI	Tang et al. (2020)	edge enhanced network, cross feature fusion
Nature Methods	Isensee et al. (2020)	self-configuring framework, extensive evaluation on 23 challenges
TMI	Cano-Espinosa et al. (2020)	biomarker regression and localization
TMI	Fang and Yan (2020)	multi-organ segmentation, multi-scale training, partially labeled data
TMI	Haghighi et al. (2021)	self-supervised learning, anatomical visual words
TMI	Zhang et al. (2020a)	interpretable learning, probability calibration
TMI	Ma et al. (2020)	geodesic active contours learning, boundary segmentation
TMI	Yan et al. (2020)	training on partially-labeled dataset, lesion detection, multi-dataset learning
TMI	Wang et al. (2020)	2.5D semantic segmentation, attention