Pancreas Segmentation
14 papers with code • 2 benchmarks • 1 datasets
Pancreas segmentation is the task of segmenting out the pancreas from medical imaging.
Convolutional neural network
Benchmarks
These leaderboards are used to track progress in Pancreas Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
Libraries
Use these libraries to find Pancreas Segmentation models and implementations
StepGame
Latest papers with no code
Diffusion Probabilistic Multi-cue Level Set for Reducing Edge Uncertainty in Pancreas Segmentation
We use the diffusion probabilistic model in the coarse segmentation stage, with the obtained probability distribution serving as both the initial localization and prior cues for the level set method.
M3BUNet: Mobile Mean Max UNet for Pancreas Segmentation on CT-Scans
Segmenting organs in CT scan images is a necessary process for multiple downstream medical image analysis tasks.
SCPMan: Shape Context and Prior Constrained Multi-scale Attention Network for Pancreatic Segmentation
Specifically, we proposed a Multi-scale Feature Extraction Module (MFE) and a Mixed-scale Attention Integration Module (MAI) to address unclear pancreas boundaries.
Detection and Segmentation of Pancreas using Morphological Snakes and Deep Convolutional Neural Networks
The segmentation task is tackled by a modified U-Net model applied on cropped data, as well as by using a morphological active contours algorithm.
Multi-organ Segmentation Network with Adversarial Performance Validator
The proposed network organically converts the 2D-coarse result to 3D high-quality segmentation masks in a coarse-to-fine manner, allowing joint optimization to improve segmentation accuracy.
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation
Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing.
Transformer-Unet: Raw Image Processing with Unet
We demonstrate our network and show our experimental results in this paper accordingly.
Multi-task Federated Learning for Heterogeneous Pancreas Segmentation
Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their data.
Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation
In this work, we design a new data-driven approach, namely Auto-FedAvg, where aggregation weights are dynamically adjusted, depending on data distributions across data silos and the current training progress of the models.
Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples
Although deep neural networks have been a dominant method for many 2D vision tasks, it is still challenging to apply them to 3D tasks, such as medical image segmentation, due to the limited amount of annotated 3D data and limited computational resources.
