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 with no code
FourierLoss: Shape-Aware Loss Function with Fourier Descriptors
Different than the previous studies, FourierLoss offers an adaptive loss function with trainable hyperparameters that control the importance of the level of the shape details that the network is enforced to learn in the training process.
$\mathrm{SAM^{Med}}$: A medical image annotation framework based on large vision model
The $\mathrm{SAM^{assist}}$ demonstrates the generalization ability of SAM to the downstream medical segmentation task using the prompt-learning approach.
Liver Segmentation in Time-resolved C-arm CT Volumes Reconstructed from Dynamic Perfusion Scans using Time Separation Technique
Perfusion imaging is a valuable tool for diagnosing and treatment planning for liver tumours.
Encoding feature supervised UNet++: Redesigning Supervision for liver and tumor segmentation
ES-UNet++ is evaluated with dataset LiTS, achieving 95. 6% for liver segmentation and 67. 4% for tumor segmentation in dice score.
Fully Automated Deep Learning-enabled Detection for Hepatic Steatosis on Computed Tomography: A Multicenter International Validation Study
To automate the process, we validated an existing artificial intelligence (AI) system for 3D liver segmentation and used it to purpose a novel method: AI-ROI, which could automatically select the ROI for attenuation measurements.
What can we learn about a generated image corrupting its latent representation?
The purpose of this work is to investigate the hypothesis that we can predict image quality based on its latent representation in the GANs bottleneck.
Efficient liver segmentation with 3D CNN using computed tomography scans
The liver is one of the most critical metabolic organs in vertebrates due to its vital functions in the human body, such as detoxification of the blood from waste products and medications.
Learning to segment with limited annotations: Self-supervised pretraining with regression and contrastive loss in MRI
In this work, we consider two pre-training approaches for driving a DL model to learn different representations using: a) regression loss that exploits spatial dependencies within an image and b) contrastive loss that exploits semantic similarity between pairs of images.
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images
In this paper, we propose a Decoupled Pyramid Correlation Network (DPC-Net) that exploits attention mechanisms to fully leverage both low- and high-level features embedded in FCN to segment liver tumor.
FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation
One of the most common methods for analyzing CT and MRI images for diagnosis and follow-up treatment is liver segmentation.
