16 research outputs found
Pulse Sequence Resilient Fast Brain Segmentation
Accurate automatic segmentation of brain anatomy from
-weighted~(-w) magnetic resonance images~(MRI) has been a
computationally intensive bottleneck in neuroimaging pipelines, with
state-of-the-art results obtained by unsupervised intensity modeling-based
methods and multi-atlas registration and label fusion. With the advent of
powerful supervised convolutional neural networks~(CNN)-based learning
algorithms, it is now possible to produce a high quality brain segmentation
within seconds. However, the very supervised nature of these methods makes it
difficult to generalize them on data different from what they have been trained
on. Modern neuroimaging studies are necessarily multi-center initiatives with a
wide variety of acquisition protocols. Despite stringent protocol harmonization
practices, it is not possible to standardize the whole gamut of MRI imaging
parameters across scanners, field strengths, receive coils etc., that affect
image contrast. In this paper we propose a CNN-based segmentation algorithm
that, in addition to being highly accurate and fast, is also resilient to
variation in the input -w acquisition. Our approach relies on building
approximate forward models of -w pulse sequences that produce a typical
test image. We use the forward models to augment the training data with test
data specific training examples. These augmented data can be used to update
and/or build a more robust segmentation model that is more attuned to the test
data imaging properties. Our method generates highly accurate, state-of-the-art
segmentation results~(overall Dice overlap=0.94), within seconds and is
consistent across a wide-range of protocols.Comment: Accepted at MICCAI 201
Contrast Adaptive Tissue Classification by Alternating Segmentation and Synthesis
Deep learning approaches to the segmentation of magnetic resonance images
have shown significant promise in automating the quantitative analysis of brain
images. However, a continuing challenge has been its sensitivity to the
variability of acquisition protocols. Attempting to segment images that have
different contrast properties from those within the training data generally
leads to significantly reduced performance. Furthermore, heterogeneous data
sets cannot be easily evaluated because the quantitative variation due to
acquisition differences often dwarfs the variation due to the biological
differences that one seeks to measure. In this work, we describe an approach
using alternating segmentation and synthesis steps that adapts the contrast
properties of the training data to the input image. This allows input images
that do not resemble the training data to be more consistently segmented. A
notable advantage of this approach is that only a single example of the
acquisition protocol is required to adapt to its contrast properties. We
demonstrate the efficacy of our approaching using brain images from a set of
human subjects scanned with two different T1-weighted volumetric protocols.Comment: 10 pages. MICCAI SASHIMI Workshop 202
A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning
In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of
organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive
generative model for whole-brain segmentation with a new spatial regularization model of tumor shape
using convolutional restricted Boltzmann machines. We demonstrate experimentally that the method is
able to adapt to image acquisitions that differ substantially from any available training data, ensuring its
applicability across treatment sites; that its tumor segmentation accuracy is comparable to that of the
current state of the art; and that it captures most organs-at-risk sufficiently well for radiation therapy
planning purposes. The proposed method may be a valuable step towards automating the delineation of
brain tumors and organs-at-risk in glioblastoma patients undergoing radiation therapy
A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
In this paper we propose a novel method for the segmentation of longitudinal
brain MRI scans of patients suffering from Multiple Sclerosis. The method
builds upon an existing cross-sectional method for simultaneous whole-brain and
lesion segmentation, introducing subject-specific latent variables to encourage
temporal consistency between longitudinal scans. It is very generally
applicable, as it does not make any prior assumptions on the scanner, the MRI
protocol, or the number and timing of longitudinal follow-up scans. Preliminary
experiments on three longitudinal datasets indicate that the proposed method
produces more reliable segmentations and detects disease effects better than
the cross-sectional method it is based upon
Partial Volume Segmentation of Brain MRI Scans of any Resolution and Contrast
Partial voluming (PV) is arguably the last crucial unsolved problem in
Bayesian segmentation of brain MRI with probabilistic atlases. PV occurs when
voxels contain multiple tissue classes, giving rise to image intensities that
may not be representative of any one of the underlying classes. PV is
particularly problematic for segmentation when there is a large resolution gap
between the atlas and the test scan, e.g., when segmenting clinical scans with
thick slices, or when using a high-resolution atlas. In this work, we present
PV-SynthSeg, a convolutional neural network (CNN) that tackles this problem by
directly learning a mapping between (possibly multi-modal) low resolution (LR)
scans and underlying high resolution (HR) segmentations. PV-SynthSeg simulates
LR images from HR label maps with a generative model of PV, and can be trained
to segment scans of any desired target contrast and resolution, even for
previously unseen modalities where neither images nor segmentations are
available at training. PV-SynthSeg does not require any preprocessing, and runs
in seconds. We demonstrate the accuracy and flexibility of the method with
extensive experiments on three datasets and 2,680 scans. The code is available
at https://github.com/BBillot/SynthSeg.Comment: accepted for MICCAI 202
Canine Oral Melanoma & Feline Oral Squamous Cell Carcinoma as a model for human medicine.
Neuroimaging to neuropathology correlation (NTNC) promises to enable the
transfer of microscopic signatures of pathology to in vivo imaging with MRI,
ultimately enhancing clinical care. NTNC traditionally requires a volumetric
MRI scan, acquired either ex vivo or a short time prior to death.
Unfortunately, ex vivo MRI is difficult and costly, and recent premortem scans
of sufficient quality are seldom available. To bridge this gap, we present
methodology to 3D reconstruct and segment full brain image volumes from brain
dissection photographs, which are routinely acquired at many brain banks and
neuropathology departments. The 3D reconstruction is achieved via a joint
registration framework, which uses a reference volume other than MRI. This
volume may represent either the sample at hand (e.g., a surface 3D scan) or the
general population (a probabilistic atlas). In addition, we present a Bayesian
method to segment the 3D reconstructed photographic volumes into 36
neuroanatomical structures, which is robust to nonuniform brightness within and
across photographs. We evaluate our methods on a dataset with 24 brains, using
Dice scores and volume correlations. The results show that dissection
photography is a valid replacement for ex vivo MRI in many volumetric analyses,
opening an avenue for MRI-free NTNC, including retrospective data. The code is
available at https://github.com/htregidgo/DissectionPhotoVolumes.Comment: Accepted at MICCAI 202