45 research outputs found
A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images
Pathologists need to combine information from differently stained pathology
slices for accurate diagnosis. Deformable image registration is a necessary
technique for fusing multi-modal pathology slices. This paper proposes a hybrid
deep feature-based deformable image registration framework for stained
pathology samples. We first extract dense feature points via the detector-based
and detector-free deep learning feature networks and perform points matching.
Then, to further reduce false matches, an outlier detection method combining
the isolation forest statistical model and the local affine correction model is
proposed. Finally, the interpolation method generates the deformable vector
field for pathology image registration based on the above matching points. We
evaluate our method on the dataset of the Non-rigid Histology Image
Registration (ANHIR) challenge, which is co-organized with the IEEE ISBI 2019
conference. Our technique outperforms the traditional approaches by 17% with
the Average-Average registration target error (rTRE) reaching 0.0034. The
proposed method achieved state-of-the-art performance and ranked 1st in
evaluating the test dataset. The proposed hybrid deep feature-based
registration method can potentially become a reliable method for pathology
image registration.Comment: 22 pages, 12 figures. This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl
Unsupervised Segmentation of Fetal Brain MRI using Deep Learning Cascaded Registration
Accurate segmentation of fetal brain magnetic resonance images is crucial for
analyzing fetal brain development and detecting potential neurodevelopmental
abnormalities. Traditional deep learning-based automatic segmentation, although
effective, requires extensive training data with ground-truth labels, typically
produced by clinicians through a time-consuming annotation process. To overcome
this challenge, we propose a novel unsupervised segmentation method based on
multi-atlas segmentation, that accurately segments multiple tissues without
relying on labeled data for training. Our method employs a cascaded deep
learning network for 3D image registration, which computes small, incremental
deformations to the moving image to align it precisely with the fixed image.
This cascaded network can then be used to register multiple annotated images
with the image to be segmented, and combine the propagated labels to form a
refined segmentation. Our experiments demonstrate that the proposed cascaded
architecture outperforms the state-of-the-art registration methods that were
tested. Furthermore, the derived segmentation method achieves similar
performance and inference time to nnU-Net while only using a small subset of
annotated data for the multi-atlas segmentation task and none for training the
network. Our pipeline for registration and multi-atlas segmentation is publicly
available at https://github.com/ValBcn/CasReg.Comment: 17 pages, 8 figures, 5 tables, paper submitted to IEEE transaction on
medical imagin
Can a single image processing algorithm work equally well across all phases of DCE-MRI?
Image segmentation and registration are said to be challenging when applied
to dynamic contrast enhanced MRI sequences (DCE-MRI). The contrast agent causes
rapid changes in intensity in the region of interest and elsewhere, which can
lead to false positive predictions for segmentation tasks and confound the
image registration similarity metric. While it is widely assumed that contrast
changes increase the difficulty of these tasks, to our knowledge no work has
quantified these effects. In this paper we examine the effect of training with
different ratios of contrast enhanced (CE) data on two popular tasks:
segmentation with nnU-Net and Mask R-CNN and registration using VoxelMorph and
VTN. We experimented further by strategically using the available datasets
through pretraining and fine tuning with different splits of data. We found
that to create a generalisable model, pretraining with CE data and fine tuning
with non-CE data gave the best result. This interesting find could be expanded
to other deep learning based image processing tasks with DCE-MRI and provide
significant improvements to the models performance
Non-iterative Coarse-to-fine Transformer Networks for Joint Affine and Deformable Image Registration
Image registration is a fundamental requirement for medical image analysis.
Deep registration methods based on deep learning have been widely recognized
for their capabilities to perform fast end-to-end registration. Many deep
registration methods achieved state-of-the-art performance by performing
coarse-to-fine registration, where multiple registration steps were iterated
with cascaded networks. Recently, Non-Iterative Coarse-to-finE (NICE)
registration methods have been proposed to perform coarse-to-fine registration
in a single network and showed advantages in both registration accuracy and
runtime. However, existing NICE registration methods mainly focus on deformable
registration, while affine registration, a common prerequisite, is still
reliant on time-consuming traditional optimization-based methods or extra
affine registration networks. In addition, existing NICE registration methods
are limited by the intrinsic locality of convolution operations. Transformers
may address this limitation for their capabilities to capture long-range
dependency, but the benefits of using transformers for NICE registration have
not been explored. In this study, we propose a Non-Iterative Coarse-to-finE
Transformer network (NICE-Trans) for image registration. Our NICE-Trans is the
first deep registration method that (i) performs joint affine and deformable
coarse-to-fine registration within a single network, and (ii) embeds
transformers into a NICE registration framework to model long-range relevance
between images. Extensive experiments with seven public datasets show that our
NICE-Trans outperforms state-of-the-art registration methods on both
registration accuracy and runtime.Comment: Accepted at International Conference on Medical Image Computing and
Computer Assisted Intervention (MICCAI 2023