What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …

Tumors exhibit genomic and phenotypic heterogeneity, which has prognostic significance
and may influence response to therapy. Imaging can quantify the spatial variation in …

▪ Abstract Image segmentation plays a crucial role in many medical-imaging applications, by
automating or facilitating the delineation of anatomical structures and other regions of …

Until the late‐twentieth century, primary anatomical sciences education was relatively
unenhanced by advanced technology and dependent on the mainstays of printed textbooks …

We introduce Atropos, an ITK-based multivariate n-class open source segmentation
algorithm distributed with ANTs (http://www. picsl. upenn. edu/ANTs). The Bayesian …

Many methods have been proposed for tissue segmentation in brain MRI scans. The
multitude of methods proposed complicates the choice of one method above others. We …

This paper has reviewed, with somewhat variable coverage, the nine MR image
segmentation techniques itemized in Table II. A wide array of approaches have been …

PURPOSE: To quantitate neuroanatomic parameters in healthy volunteers and to compare
the values with normative values from postmortem studies. MATERIALS AND METHODS …

Intensity-based classification of MR images has proven problematic, even when advanced
techniques are used. Intrascan and interscan intensity inhomogeneities are a common …

A statistical model is presented that represents the distributions of major tissue classes in
single-channel magnetic resonance (MR) cerebral images. Using the model, cerebral …