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Found 5 papers, 2 papers with code
Regression Forest-Based Atlas Localization and Direction Specific Atlas Generation for Pancreas Segmentation
This paper proposes a fully automated atlas-based pancreas segmentation method from CT volumes utilizing atlas localization by regression forest and atlas generation using blood vessel information.
3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation
We estimate the position and the size of the pancreas (localized) from global features by regression forests.
An application of cascaded 3D fully convolutional networks for medical image segmentation
In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures (ranging from the large organs to thin vessels) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training class-specific models.
Ranked #2 on
3D Medical Imaging Segmentation
on TCIA Pancreas-CT
Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks
Pancreas segmentation in computed tomography imaging has been historically difficult for automated methods because of the large shape and size variations between patients.
Hierarchical 3D fully convolutional networks for multi-organ segmentation
In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of seven abdominal structures (artery, vein, liver, spleen, stomach, gallbladder, and pancreas) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training organ-specific models.
