Liver Segmentation
26 papers with code • 1 benchmarks • 2 datasets
Most implemented papers
Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion
For the label fusion, we design a similarity estimation network (SimNet), which estimates the fusion weight of each atlas by measuring its similarity to the target image.
Transformer based Generative Adversarial Network for Liver Segmentation
The premise behind this choice is that the self-attention mechanism of the Transformers allows the network to aggregate the high dimensional feature and provide global information modeling.
Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging
This paper shows the potential of segmenting the liver from CT, CBCT, and CBCT TST, learning from the available limited training data, which can possibly be used in the future for the visualisation and evaluation of the perfusion maps for the treatment evaluation of liver diseases.
Mci-net: multi-scale context integrated network for liver ct image segmentation
Owing to the various object scales and high similarity with the surrounding organs (e. g., kidney, stomach, and spleen), it is difficult to accurately segment the liver region from the abdominal computed tomography images.
From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentation
In medical image segmentation, domain generalization poses a significant challenge due to domain shifts caused by variations in data acquisition devices and other factors.
CT Liver Segmentation via PVT-based Encoding and Refined Decoding
Accurate liver segmentation from CT scans is essential for effective diagnosis and treatment planning.