3 research outputs found
Reproducibility Evaluation of SLANT Whole Brain Segmentation Across Clinical Magnetic Resonance Imaging Protocols
Whole brain segmentation on structural magnetic resonance imaging (MRI) is
essential for understanding neuroanatomical-functional relationships.
Traditionally, multi-atlas segmentation has been regarded as the standard
method for whole brain segmentation. In past few years, deep convolutional
neural network (DCNN) segmentation methods have demonstrated their advantages
in both accuracy and computational efficiency. Recently, we proposed the
spatially localized atlas network tiles (SLANT) method, which is able to
segment a 3D MRI brain scan into 132 anatomical regions. Commonly, DCNN
segmentation methods yield inferior performance under external validations,
especially when the testing patterns were not presented in the training
cohorts. Recently, we obtained a clinically acquired, multi-sequence MRI brain
cohort with 1480 clinically acquired, de-identified brain MRI scans on 395
patients using seven different MRI protocols. Moreover, each subject has at
least two scans from different MRI protocols. Herein, we assess the SLANT
method's intra- and inter-protocol reproducibility. SLANT achieved less than
0.05 coefficient of variation (CV) for intra-protocol experiments and less than
0.15 CV for inter-protocol experiments. The results show that the SLANT method
achieved high intra- and inter- protocol reproducibility.Comment: To appear in SPIE Medical Imaging 201
Generalizing Deep Whole Brain Segmentation for Pediatric and Post-Contrast MRI with Augmented Transfer Learning
Generalizability is an important problem in deep neural networks, especially
in the context of the variability of data acquisition in clinical magnetic
resonance imaging (MRI). Recently, the Spatially Localized Atlas Network Tiles
(SLANT) approach has been shown to effectively segment whole brain non-contrast
T1w MRI with 132 volumetric labels. Enhancing generalizability of SLANT would
enable broader application of volumetric assessment in multi-site studies.
Transfer learning (TL) is commonly used to update the neural network weights
for local factors; yet, it is commonly recognized to risk degradation of
performance on the original validation/test cohorts. Here, we explore TL by
data augmentation to address these concerns in the context of adapting SLANT to
anatomical variation and scanning protocol. We consider two datasets: First, we
optimize for age with 30 T1w MRI of young children with manually corrected
volumetric labels, and accuracy of automated segmentation defined relative to
the manually provided truth. Second, we optimize for acquisition with 36 paired
datasets of pre- and post-contrast clinically acquired T1w MRI, and accuracy of
the post-contrast segmentations assessed relative to the pre-contrast automated
assessment. For both studies, we augment the original TL step of SLANT with
either only the new data or with both original and new data. Over baseline
SLANT, both approaches yielded significantly improved performance (signed rank
tests; pediatric: 0.89 vs. 0.82 DSC, p<0.001; contrast: 0.80 vs 0.76, p<0.001).
The performance on the original test set decreased with the new-data only
transfer learning approach, so data augmentation was superior to strict
transfer learning
Construction and Usage of a Human Body Common Coordinate Framework Comprising Clinical, Semantic, and Spatial Ontologies
The National Institutes of Health's (NIH) Human Biomolecular Atlas Program
(HuBMAP) aims to create a comprehensive high-resolution atlas of all the cells
in the healthy human body. Multiple laboratories across the United States are
collecting tissue specimens from different organs of donors who vary in sex,
age, and body size. Integrating and harmonizing the data derived from these
samples and 'mapping' them into a common three-dimensional (3D) space is a
major challenge. The key to making this possible is a 'Common Coordinate
Framework' (CCF), which provides a semantically annotated, 3D reference system
for the entire body. The CCF enables contributors to HuBMAP to 'register'
specimens and datasets within a common spatial reference system, and it
supports a standardized way to query and 'explore' data in a spatially and
semantically explicit manner. [...] This paper describes the construction and
usage of a CCF for the human body and its reference implementation in HuBMAP.
The CCF consists of (1) a CCF Clinical Ontology, which provides metadata about
the specimen and donor (the 'who'); (2) a CCF Semantic Ontology, which
describes 'what' part of the body a sample came from and details anatomical
structures, cell types, and biomarkers (ASCT+B); and (3) a CCF Spatial
Ontology, which indicates 'where' a tissue sample is located in a 3D coordinate
system. An initial version of all three CCF ontologies has been implemented for
the first HuBMAP Portal release. It was successfully used by Tissue Mapping
Centers to semantically annotate and spatially register 48 kidney and spleen
tissue blocks. The blocks can be queried and explored in their clinical,
semantic, and spatial context via the CCF user interface in the HuBMAP Portal.Comment: 24 pages with SI, 6 figures, 5 table