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
Most implemented papers
Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentation
MarielleJansen/Liver-segmentation
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In this study, the optimal input configuration of DCE MR images for convolutional neural networks (CNNs) is studied.
Liver segmentation and metastases detection in MR images using convolutional neural networks
MarielleJansen/Liver-metastases-detection
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Primary tumors have a high likelihood of developing metastases in the liver and early detection of these metastases is crucial for patient outcome.
KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation
jeya-maria-jose/KiU-Net-pytorch
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To overcome this issue, we propose using an overcomplete convolutional architecture where we project our input image into a higher dimension such that we constrain the receptive field from increasing in the deep layers of the network.
Upgraded W-Net with Attention Gates and its Application in Unsupervised 3D Liver Segmentation
MittaDhanunjaya/Master-Thesis
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Segmentation of biomedical images can assist radiologists to make a better diagnosis and take decisions faster by helping in the detection of abnormalities, such as tumors.
Automatic Liver Segmentation from CT Images Using Deep Learning Algorithms: A Comparative Study
Recently, with the development of Deep Learning (DL) algorithms, automatic organ segmentation has been gathered lots of attention from the researchers.
Deep Implicit Statistical Shape Models for 3D Medical Image Delineation
ashstuff/dissm
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DISSMs use a deep implicit surface representation to produce a compact and descriptive shape latent space that permits statistical models of anatomical variance.
Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration
Our experimental results on in-house TACE patient data demonstrated that our APA2Seg-Net can generate robust CBCT and MR liver segmentation, and the anatomy-guided registration framework with these segmenters can provide high-quality multimodal registrations.
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.
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.
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.
