Expected Label Value (ELV) Computation for Multi-Atlas Image Soft-Segmentation
This is the public Matlab implementation for medical image soft-segmentation using the atlas-based expected label value (ELV) approach proposed by Aganj and Fischl (IEEE ISBI 2019; bioRxiv 2020). This approach considers the probability of all possible atlas-to-image transformations and computes the ELV, thereby not relying merely on the transformation deemed optimal by the registration method. This is done without deformable registration, thus avoiding the associated computational costs. A short tutorial is included in EXAMPLE.m.
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Diffusion MRI Orientation Distribution Function in Constant Solid Angle (CSA-ODF) and Hough-Transform Tractography
Image Segmentation Based on the Local Center of Mass Computation
Mid-Space-Independent Deformable Image Registration
Quantification of Structural Brain Connectivity via a Conductance Model
Segmentation-based Multimodal Rigid Image Registration
Tissue Thickness Estimation via Minimum Line Integrals
Wavelet-based Image Fusion
Image Segmentation Based on the Local Center of Mass Computation
Mid-Space-Independent Deformable Image Registration
Quantification of Structural Brain Connectivity via a Conductance Model
Segmentation-based Multimodal Rigid Image Registration
Tissue Thickness Estimation via Minimum Line Integrals
Wavelet-based Image Fusion
Recent Activity - Documents
Article (pre-print) describing the methods. posted by Iman Aganj on Oct 17, 2020
ISBI paper posted by Iman Aganj on Oct 17, 2020
Recent Activity - Forums
Welcome to Open-Discussion posted by Iman Aganj on Oct 16, 2020