18,362 research outputs found
Learning Deep Similarity Metric for 3D MR-TRUS Registration
Purpose: The fusion of transrectal ultrasound (TRUS) and magnetic resonance
(MR) images for guiding targeted prostate biopsy has significantly improved the
biopsy yield of aggressive cancers. A key component of MR-TRUS fusion is image
registration. However, it is very challenging to obtain a robust automatic
MR-TRUS registration due to the large appearance difference between the two
imaging modalities. The work presented in this paper aims to tackle this
problem by addressing two challenges: (i) the definition of a suitable
similarity metric and (ii) the determination of a suitable optimization
strategy.
Methods: This work proposes the use of a deep convolutional neural network to
learn a similarity metric for MR-TRUS registration. We also use a composite
optimization strategy that explores the solution space in order to search for a
suitable initialization for the second-order optimization of the learned
metric. Further, a multi-pass approach is used in order to smooth the metric
for optimization.
Results: The learned similarity metric outperforms the classical mutual
information and also the state-of-the-art MIND feature based methods. The
results indicate that the overall registration framework has a large capture
range. The proposed deep similarity metric based approach obtained a mean TRE
of 3.86mm (with an initial TRE of 16mm) for this challenging problem.
Conclusion: A similarity metric that is learned using a deep neural network
can be used to assess the quality of any given image registration and can be
used in conjunction with the aforementioned optimization framework to perform
automatic registration that is robust to poor initialization.Comment: To appear on IJCAR
Robust Registration of Calcium Images by Learned Contrast Synthesis
Multi-modal image registration is a challenging task that is vital to fuse
complementary signals for subsequent analyses. Despite much research into cost
functions addressing this challenge, there exist cases in which these are
ineffective. In this work, we show that (1) this is true for the registration
of in-vivo Drosophila brain volumes visualizing genetically encoded calcium
indicators to an nc82 atlas and (2) that machine learning based contrast
synthesis can yield improvements. More specifically, the number of subjects for
which the registration outright failed was greatly reduced (from 40% to 15%) by
using a synthesized image
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context
We present an efficient deep learning approach for the challenging task of
tumor segmentation in multisequence MR images. In recent years, Convolutional
Neural Networks (CNN) have achieved state-of-the-art performances in a large
variety of recognition tasks in medical imaging. Because of the considerable
computational cost of CNNs, large volumes such as MRI are typically processed
by subvolumes, for instance slices (axial, coronal, sagittal) or small 3D
patches. In this paper we introduce a CNN-based model which efficiently
combines the advantages of the short-range 3D context and the long-range 2D
context. To overcome the limitations of specific choices of neural network
architectures, we also propose to merge outputs of several cascaded 2D-3D
models by a voxelwise voting strategy. Furthermore, we propose a network
architecture in which the different MR sequences are processed by separate
subnetworks in order to be more robust to the problem of missing MR sequences.
Finally, a simple and efficient algorithm for training large CNN models is
introduced. We evaluate our method on the public benchmark of the BRATS 2017
challenge on the task of multiclass segmentation of malignant brain tumors. Our
method achieves good performances and produces accurate segmentations with
median Dice scores of 0.918 (whole tumor), 0.883 (tumor core) and 0.854
(enhancing core). Our approach can be naturally applied to various tasks
involving segmentation of lesions or organs.Comment: Submitted to the journal Computerized Medical Imaging and Graphic
HeMIS: Hetero-Modal Image Segmentation
We introduce a deep learning image segmentation framework that is extremely
robust to missing imaging modalities. Instead of attempting to impute or
synthesize missing data, the proposed approach learns, for each modality, an
embedding of the input image into a single latent vector space for which
arithmetic operations (such as taking the mean) are well defined. Points in
that space, which are averaged over modalities available at inference time, can
then be further processed to yield the desired segmentation. As such, any
combinatorial subset of available modalities can be provided as input, without
having to learn a combinatorial number of imputation models. Evaluated on two
neurological MRI datasets (brain tumors and MS lesions), the approach yields
state-of-the-art segmentation results when provided with all modalities;
moreover, its performance degrades remarkably gracefully when modalities are
removed, significantly more so than alternative mean-filling or other synthesis
approaches.Comment: Accepted as an oral presentation at MICCAI 201
Groupwise Multimodal Image Registration using Joint Total Variation
In medical imaging it is common practice to acquire a wide range of
modalities (MRI, CT, PET, etc.), to highlight different structures or
pathologies. As patient movement between scans or scanning session is
unavoidable, registration is often an essential step before any subsequent
image analysis. In this paper, we introduce a cost function based on joint
total variation for such multimodal image registration. This cost function has
the advantage of enabling principled, groupwise alignment of multiple images,
whilst being insensitive to strong intensity non-uniformities. We evaluate our
algorithm on rigidly aligning both simulated and real 3D brain scans. This
validation shows robustness to strong intensity non-uniformities and low
registration errors for CT/PET to MRI alignment. Our implementation is publicly
available at https://github.com/brudfors/coregistration-njtv
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