119 research outputs found
Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders
Lesion detection in brain Magnetic Resonance Images (MRI) remains a
challenging task. State-of-the-art approaches are mostly based on supervised
learning making use of large annotated datasets. Human beings, on the other
hand, even non-experts, can detect most abnormal lesions after seeing a handful
of healthy brain images. Replicating this capability of using prior information
on the appearance of healthy brain structure to detect lesions can help
computers achieve human level abnormality detection, specifically reducing the
need for numerous labeled examples and bettering generalization of previously
unseen lesions. To this end, we study detection of lesion regions in an
unsupervised manner by learning data distribution of brain MRI of healthy
subjects using auto-encoder based methods. We hypothesize that one of the main
limitations of the current models is the lack of consistency in latent
representation. We propose a simple yet effective constraint that helps mapping
of an image bearing lesion close to its corresponding healthy image in the
latent space. We use the Human Connectome Project dataset to learn distribution
of healthy-appearing brain MRI and report improved detection, in terms of AUC,
of the lesions in the BRATS challenge dataset.Comment: 9 pages, 5 figures, accepted at MIDL 201
Temporal Interpolation via Motion Field Prediction
Navigated 2D multi-slice dynamic Magnetic Resonance (MR) imaging enables high
contrast 4D MR imaging during free breathing and provides in-vivo observations
for treatment planning and guidance. Navigator slices are vital for
retrospective stacking of 2D data slices in this method. However, they also
prolong the acquisition sessions. Temporal interpolation of navigator slices an
be used to reduce the number of navigator acquisitions without degrading
specificity in stacking. In this work, we propose a convolutional neural
network (CNN) based method for temporal interpolation via motion field
prediction. The proposed formulation incorporates the prior knowledge that a
motion field underlies changes in the image intensities over time. Previous
approaches that interpolate directly in the intensity space are prone to
produce blurry images or even remove structures in the images. Our method
avoids such problems and faithfully preserves the information in the image.
Further, an important advantage of our formulation is that it provides an
unsupervised estimation of bi-directional motion fields. We show that these
motion fields can be used to halve the number of registrations required during
4D reconstruction, thus substantially reducing the reconstruction time.Comment: Submitted to 1st Conference on Medical Imaging with Deep Learning
(MIDL 2018), Amsterdam, The Netherland
Unsupervised Lesion Detection via Image Restoration with a Normative Prior
Unsupervised lesion detection is a challenging problem that requires
accurately estimating normative distributions of healthy anatomy and detecting
lesions as outliers without training examples. Recently, this problem has
received increased attention from the research community following the advances
in unsupervised learning with deep learning. Such advances allow the estimation
of high-dimensional distributions, such as normative distributions, with higher
accuracy than previous methods.The main approach of the recently proposed
methods is to learn a latent-variable model parameterized with networks to
approximate the normative distribution using example images showing healthy
anatomy, perform prior-projection, i.e. reconstruct the image with lesions
using the latent-variable model, and determine lesions based on the differences
between the reconstructed and original images. While being promising, the
prior-projection step often leads to a large number of false positives. In this
work, we approach unsupervised lesion detection as an image restoration problem
and propose a probabilistic model that uses a network-based prior as the
normative distribution and detect lesions pixel-wise using MAP estimation. The
probabilistic model punishes large deviations between restored and original
images, reducing false positives in pixel-wise detections. Experiments with
gliomas and stroke lesions in brain MRI using publicly available datasets show
that the proposed approach outperforms the state-of-the-art unsupervised
methods by a substantial margin, +0.13 (AUC), for both glioma and stroke
detection. Extensive model analysis confirms the effectiveness of MAP-based
image restoration.Comment: Extended version of 'Unsupervised Lesion Detection via Image
Restoration with a Normative Prior' (MIDL2019
A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic Capsule Robots
In the gastrointestinal (GI) tract endoscopy field, ingestible wireless
capsule endoscopy is considered as a minimally invasive novel diagnostic
technology to inspect the entire GI tract and to diagnose various diseases and
pathologies. Since the development of this technology, medical device companies
and many groups have made significant progress to turn such passive capsule
endoscopes into robotic active capsule endoscopes to achieve almost all
functions of current active flexible endoscopes. However, the use of robotic
capsule endoscopy still has some challenges. One such challenge is the precise
localization of such active devices in 3D world, which is essential for a
precise three-dimensional (3D) mapping of the inner organ. A reliable 3D map of
the explored inner organ could assist the doctors to make more intuitive and
correct diagnosis. In this paper, we propose to our knowledge for the first
time in literature a visual simultaneous localization and mapping (SLAM) method
specifically developed for endoscopic capsule robots. The proposed RGB-Depth
SLAM method is capable of capturing comprehensive dense globally consistent
surfel-based maps of the inner organs explored by an endoscopic capsule robot
in real time. This is achieved by using dense frame-to-model camera tracking
and windowed surfelbased fusion coupled with frequent model refinement through
non-rigid surface deformations
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