AutoSeg

AutoSeg is a novel C++ based application developped at UNC-Chapel Hill that performs automatic brain tissue classification and structural segmentation.

AutoSeg is designed for use with human and non-human primate pediatric, adolescent and adult data.

AutoSeg uses a BatchMake pipeline script that includes the main steps of the framework entailing N4 bias field correction, rigid registration to a common coordinate image, tissue segmentation, skull-stripping, intensity rescaling, atlas-based registration, subcortical segmentation and lobar parcellation, regional cortical thickness and intensity statistics. AutoSeg allows efficient batch processing and grid computing to process large datasets and provides quality control visualizations via Slicer3 MRML scenes.
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Specifications

Category:Expectation Minimization, Labeling, Volumetric Analysis, Image-to-Image, Image-to-Template, Spline Interpolation, Nonlinear Warp, Workflow
License:Apache License 2.0
Development Status:5 - Production/Stable/Mature
Diagnosis:Parkinson Disease
Domain:MR
Environment:Win32 (MS Windows), X11 Applications
Intended Audience:Developers, End Users
Natural Language:English
Operating System:MacOS, Windows, Linux
Programming Language:C++
Supported Data Format:ANALYZE, Nrrd, Other Format

Recent Activity

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Document Activity

PubMed Mentions documentation

Optimization of long-distance PCR using a transposon-based model system. posted by Nina Preuss on Dec 16, 2017

Document Activity

PubMed Mentions documentation

[An automatic color correction algorithm for digital human body sections]. posted by Nina Preuss on Dec 16, 2017

Document Activity

PubMed Mentions documentation

Automatic segmentation of whole breast using atlas approach and deformable image registration. posted by Nina Preuss on Dec 16, 2017

Document Activity

PubMed Mentions documentation

A qualitative and a quantitative analysis of an auto-segmentation module for prostate cancer. posted by Nina Preuss on Dec 16, 2017

Document Activity

PubMed Mentions documentation

Developmental implications of nonlinear phonological theory. posted by Nina Preuss on Dec 16, 2017

Document Activity

PubMed Mentions documentation

Reconstruction of brachytherapy seed positions and orientations from cone-beam CT x-ray projections via a novel iterative forward projection matching method. posted by Nina Preuss on Dec 16, 2017

Document Activity

PubMed Mentions documentation

Clinical validation of atlas-based auto-segmentation of multiple target volumes and normal tissue (swallowing/mastication) structures in the head and neck. posted by Nina Preuss on Dec 16, 2017

Document Activity

PubMed Mentions documentation

Localizing intracavitary brachytherapy applicators from cone-beam CT x-ray projections via a novel iterative forward projection matching algorithm. posted by Nina Preuss on Dec 16, 2017

Document Activity

PubMed Mentions documentation

Auto-segmentation of normal and target structures in head and neck CT images: a feature-driven model-based approach. posted by Nina Preuss on Dec 16, 2017

Document Activity

PubMed Mentions documentation

Serum cholesterol and nigrostriatal R2* values in Parkinson's disease. posted by Nina Preuss on Dec 16, 2017