Liver Segmentation
26 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Liver Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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LiTS17
Radio-Freqency Ultrasound volume dataset for pre-clinical liver tumors
Latest papers
CT Liver Segmentation via PVT-based Encoding and Refined Decoding
Accurate liver segmentation from CT scans is essential for effective diagnosis and treatment planning.
From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentation
wenruxue/detta
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In medical image segmentation, domain generalization poses a significant challenge due to domain shifts caused by variations in data acquisition devices and other factors.
Mci-net: multi-scale context integrated network for liver ct image segmentation
Xie-Xiwang/MCI-Net
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Computers and Electrical Engineering 2023
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.
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.
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.
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.
Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images
The computer-aided diagnosis of focal liver lesions (FLLs) can help improve workflow and enable correct diagnoses; FLL detection is the first step in such a computer-aided diagnosis.
OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data
christophreich1996/oss-net
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Occupancy networks (O-Nets) are an alternative for which the data is represented continuously in a function space and 3D shapes are learned as a continuous decision boundary.
Training on Polar Image Transformations Improves Biomedical Image Segmentation
marinbenc/medical-polar-training
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IEEE Access 2021
We show that our method produces state-of-the-art results for lesion, liver, and polyp segmentation and performs better than most common neural network architectures for biomedical image segmentation.
Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning
Captain-Hong/UDA-via-joint-adversarial-learning-and-self-learning
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In this work, we report a novel unsupervised domain adaptation framework for cross-modality liver segmentation via joint adversarial learning and self-learning.
