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Found 11 papers, 2 papers with code
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.
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.
On the influence of Dice loss function in multi-class organ segmentation of abdominal CT using 3D fully convolutional networks
Deep learning-based methods achieved impressive results for the segmentation of medical images.
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
Deep learning and its application to medical image segmentation
However, recent advances in deep learning have made it possible to significantly improve the performance of image recognition and semantic segmentation methods in the field of computer vision.
A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation
Recent advances in deep learning, like 3D fully convolutional networks (FCNs), have improved the state-of-the-art in dense semantic segmentation of medical images.
Precise Estimation of Renal Vascular Dominant Regions Using Spatially Aware Fully Convolutional Networks, Tensor-Cut and Voronoi Diagrams
Then we generate a Voronoi diagram to estimate the renal vascular dominant regions based on the segmented kidney and renal arteries.
COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty
We utilize the scale uncertainty among various receptive field sizes of a segmentation FCN to obtain infection regions.
Lung infection and normal region segmentation from CT volumes of COVID-19 cases
Our method recognizes and segments lung normal and infection regions in CT volumes.
Semi-automated Virtual Unfolded View Generation Method of Stomach from CT Volumes
In this paper, we propose a semi-automated method for generating VU views of the stomach.
Identifying Suspicious Regions of Covid-19 by Abnormality-Sensitive Activation Mapping
This paper presents a fully-automated method for the identification of suspicious regions of a coronavirus disease (COVID-19) on chest CT volumes.
