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SOTA
Liver Segmentation
Liver Segmentation On Lits2017
Liver Segmentation On Lits2017
Metriken
Dice
HD
IoU
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
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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