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Liver Segmentation On Lits2017

Métriques

Dice

HD

IoU

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Dice	HD	IoU	Paper Title	Repository
PVTFormer	86.78	3.50	78.46	CT Liver Segmentation via PVT-based Encoding and Refined Decoding
H-DenseUnet Liver	96.5	-	-	H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
KiU-Net 3D Liver	-	-	89.46	KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation
Polar U-Net	93.02	-	89.85	Training on Polar Image Transformations Improves Biomedical Image Segmentation
ModelGenesis	91.13	-	79.52	Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
KiU-Net 3D	94.23	-	-	KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation
H-DenseUnet Lession	82.4	-	-	H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
U-Net LiS (MICCAI 17)	94	-	-	Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss function
Semantic Genesis	92.27	-	85.6	Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration

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