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
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.
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.
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.
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.
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.
Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration
To this end, we train deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a semantics-enriched, general-purpose, pre-trained 3D model, named Semantic Genesis.
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.
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.
Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of our Models Genesis for 3D medical imaging.
Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation
nct_tso_public/laparoscopic-image-2-image-translation
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We show that this data set can be used to train models for the task of liver segmentation of laparoscopic images.
