Medical Image Segmentation Techniques

The paper introduces a deep learning method for automatic quality control of stereotaxic MRI registration, achieving higher accuracy than manual assessment.

The paper constructs a high-resolution probabilistic in vivo atlas of human subcortical brain nuclei using MRI data from 168 adults and provides software tools for atlas development.

The paper presents a deep learning method for tissue classification in infant MRI scans and applies it to the iSeg-2017 challenge data.

MIMoSA improves automated white matter lesion segmentation in multiple sclerosis by integrating multimodal MRI data through inter-modal coupling regression and local logistic regression.

This study compares diffeomorphic and non-diffeomorphic methods for creating average anatomical templates and finds that diffeomorphic frameworks do not necessarily improve accuracy.

The paper introduces ATLAS, a dataset of 304 T1-weighted MRIs with manual lesion segmentations to improve lesion segmentation algorithm accuracy.

The paper presents a 3D digital brain atlas of the common marmoset monkey with MRI and Nissl histology data, offering improved segmentation and file format for neuroscience research.

The paper introduces a fully automated pipeline for neonatal cortical surface reconstruction from MRI, achieving high accuracy in 85% of cases.

A linearly scaling groupwise method jointly estimates mean brain shape and longitudinal change across 275 neonates (35 to 44 weeks post-menstrual age), yielding a temporally consistent morphological atlas.

This study uses an Irregularity Age Map to adapt transfer learning for brain lesion segmentation and predict WMH progression/regression without manual labels.

This paper introduces a computational method to unfold the hippocampus, enabling 2D mapping of subfields and revealing structural regularities validated by histology.

This paper introduces a new regularization method for cortical surface alignment in brain imaging, improving the robustness and biological interpretability of multi-modal data alignment.

This study improves automated vein segmentation in MRI by combining susceptibility-weighted images, quantitative susceptibility maps, and a vein atlas into a composite vein image, showing significant performance gains.

This paper develops a contrast matching algorithm to align MDA models of the human hippocampus with different contrasts, creating a high-resolution in vivo model for improved segmentation and disease detection.

The paper presents a high-resolution MRI template and analysis tools for macaque brains, enabling standardized processing and comparison across subjects and studies.

Mindcontrol is a web application that enables collaborative quality control of neuroimaging data through visualization, annotation, and integration with datasets.

Open source Mindboggle pipeline extracts cortical folds, sulci and fundi and computes diverse shape measures, yielding normative morphometry for 101 manually labeled healthy human brains.