Search Results for author:
Found 6 papers, 0 papers with code
Spatial Aggregation of Holistically-Nested Convolutional Neural Networks for Automated Pancreas Localization and Segmentation
Accurate and automatic organ segmentation from 3D radiological scans is an important yet challenging problem for medical image analysis.
Ranked #1 on
3D Medical Imaging Segmentation
on TCIA Pancreas-CT
Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation
Accurate automatic organ segmentation is an important yet challenging problem for medical image analysis.
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
We propose and evaluate several variations of deep ConvNets in the context of hierarchical, coarse-to-fine classification on image patches and regions, i. e. superpixels.
A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling
We present a bottom-up approach for pancreas segmentation in abdominal CT scans that is based on a hierarchy of information propagation by classifying image patches at different resolutions; and cascading superpixels.
Deep convolutional networks for pancreas segmentation in CT imaging
We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing).
A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans
Organ segmentation is a prerequisite for a computer-aided diagnosis (CAD) system to detect pathologies and perform quantitative analysis.
