444 research outputs found
Interactive Whole-Heart Segmentation in Congenital Heart Disease
We present an interactive algorithm to segment the heart chambers and epicardial surfaces, including the great vessel walls, in pediatric cardiac MRI of congenital heart disease. Accurate whole-heart segmentation is necessary to create patient-specific 3D heart models for surgical planning in the presence of complex heart defects. Anatomical variability due to congenital defects precludes fully automatic atlas-based segmentation. Our interactive segmentation method exploits expert segmentations of a small set of short-axis slice regions to automatically delineate the remaining volume using patch-based segmentation. We also investigate the potential of active learning to automatically solicit user input in areas where segmentation error is likely to be high. Validation is performed on four subjects with double outlet right ventricle, a severe congenital heart defect. We show that strategies asking the user to manually segment regions of interest within short-axis slices yield higher accuracy with less user input than those querying entire short-axis sliceNatural Sciences and Engineering Research Council of Canada (Alexander Graham Bell Canada Graduate Scholarships-Doctoral Program (CGS D))Wistron CorporationNational Institute for Biomedical Imaging and Bioengineering (U.S.) (NAMIC U54-EB005149)Boston Children's Hospital (Translational Research Program Fellowship)Boston Children's Hospital. Office of Faculty DevelopmentHarvard Catalys
Whole heart segmentation from CT images using 3D U-Net architecture
Recent studies have demonstrated the importance of neural networks in medical image processing and analysis. However, their great efficiency in segmentation tasks is highly dependent on the amount of training data. When these networks are used on small datasets, the process of data augmentation can be very significant. We propose a convolutional neural network approach for the whole heart segmentation which is based upon the 3D U-Net architecture and incorporates principle component analysis as an additional data augmentation technique. The network is trained end-to-end i.e. no pre-trained network is required. Evaluation of the proposed approach is performed on 20 3D CT images from MICCAI 2017 Multi-Modality Whole Heart Segmentation Challenge dataset, divided into 15 training and 5 validation images. Final segmentation results show a high Dice coefficient overlap to ground truth, indicating that the proposed approach is competitive to state-of-the-art. Additionally, we provide the discussion of the influence of different learning rates on the final segmentation results
Automatic whole heart segmentation based on image registration
Whole heart segmentation can provide important morphological information of the heart, potentially
enabling the development of new clinical applications and the planning and guidance
of cardiac interventional procedures. This information can be extracted from medical images,
such as these of magnetic resonance imaging (MRI), which is becoming a routine modality
for the determination of cardiac morphology. Since manual delineation is labour intensive and
subject to observer variation, it is highly desirable to develop an automatic method. However,
automating the process is complicated by the large shape variation of the heart and limited
quality of the data. The aim of this work is to develop an automatic and robust segmentation
framework from cardiac MRI while overcoming these difficulties.
The main challenge of this segmentation is initialisation of the substructures and inclusion
of shape constraints. We propose the locally affine registration method (LARM) and the freeform
deformations with adaptive control point status to tackle the challenge. They are applied
to the atlas propagation based segmentation framework, where the multi-stage scheme is used to
hierarchically increase the degree of freedom. In this segmentation framework, it is also needed
to compute the inverse transformation for the LARM registration. Therefore, we propose a
generic method, using Dynamic Resampling And distance Weighted interpolation (DRAW), for
inverting dense displacements. The segmentation framework is validated on a clinical dataset
which includes nine pathologies.
To further improve the nonrigid registration against local intensity distortions in the images,
we propose a generalised spatial information encoding scheme and the spatial information
encoded mutual information (SIEMI) registration. SIEMI registration is applied to the segmentation
framework to improve the accuracy. Furthermore, to demonstrate the general applicability
of SIEMI registration, we apply it to the registration of cardiac MRI, brain MRI, and the
contrast enhanced MRI of the liver. SIEMI registration is shown to perform well and achieve
significantly better accuracy compared to the registration using normalised mutual information
Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data
Three-dimensional medical image segmentation is one of the most important
problems in medical image analysis and plays a key role in downstream diagnosis
and treatment. Recent years, deep neural networks have made groundbreaking
success in medical image segmentation problem. However, due to the high
variance in instrumental parameters, experimental protocols, and subject
appearances, the generalization of deep learning models is often hindered by
the inconsistency in medical images generated by different machines and
hospitals. In this work, we present StyleSegor, an efficient and easy-to-use
strategy to alleviate this inconsistency issue. Specifically, neural style
transfer algorithm is applied to unlabeled data in order to minimize the
differences in image properties including brightness, contrast, texture, etc.
between the labeled and unlabeled data. We also apply probabilistic adjustment
on the network output and integrate multiple predictions through ensemble
learning. On a publicly available whole heart segmentation benchmarking dataset
from MICCAI HVSMR 2016 challenge, we have demonstrated an elevated dice
accuracy surpassing current state-of-the-art method and notably, an improvement
of the total score by 29.91\%. StyleSegor is thus corroborated to be an
accurate tool for 3D whole heart segmentation especially on highly inconsistent
data, and is available at https://github.com/horsepurve/StyleSegor.Comment: 22nd International Conference on Medical Image Computing and Computer
Assisted Intervention (MICCAI 2019) early accep
Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT
Non-invasive detection of cardiovascular disorders from radiology scans
requires quantitative image analysis of the heart and its substructures. There
are well-established measurements that radiologists use for diseases assessment
such as ejection fraction, volume of four chambers, and myocardium mass. These
measurements are derived as outcomes of precise segmentation of the heart and
its substructures. The aim of this paper is to provide such measurements
through an accurate image segmentation algorithm that automatically delineates
seven substructures of the heart from MRI and/or CT scans. Our proposed method
is based on multi-planar deep convolutional neural networks (CNN) with an
adaptive fusion strategy where we automatically utilize complementary
information from different planes of the 3D scans for improved delineations.
For CT and MRI, we have separately designed three CNNs (the same architectural
configuration) for three planes, and have trained the networks from scratch for
voxel-wise labeling for the following cardiac structures: myocardium of left
ventricle (Myo), left atrium (LA), left ventricle (LV), right atrium (RA),
right ventricle (RV), ascending aorta (Ao), and main pulmonary artery (PA). We
have evaluated the proposed method with 4-fold-cross validation on the
multi-modality whole heart segmentation challenge (MM-WHS 2017) dataset. The
precision and dice index of 0.93 and 0.90, and 0.87 and 0.85 were achieved for
CT and MR images, respectively. While a CT volume was segmented about 50
seconds, an MRI scan was segmented around 17 seconds with the GPUs/CUDA
implementation.Comment: The paper is accepted to STACOM 201
A Fine-Grain Error Map Prediction and Segmentation Quality Assessment Framework for Whole-Heart Segmentation
When introducing advanced image computing algorithms, e.g., whole-heart
segmentation, into clinical practice, a common suspicion is how reliable the
automatically computed results are. In fact, it is important to find out the
failure cases and identify the misclassified pixels so that they can be
excluded or corrected for the subsequent analysis or diagnosis. However, it is
not a trivial problem to predict the errors in a segmentation mask when ground
truth (usually annotated by experts) is absent. In this work, we attempt to
address the pixel-wise error map prediction problem and the per-case mask
quality assessment problem using a unified deep learning (DL) framework.
Specifically, we first formalize an error map prediction problem, then we
convert it to a segmentation problem and build a DL network to tackle it. We
also derive a quality indicator (QI) from a predicted error map to measure the
overall quality of a segmentation mask. To evaluate the proposed framework, we
perform extensive experiments on a public whole-heart segmentation dataset,
i.e., MICCAI 2017 MMWHS. By 5-fold cross validation, we obtain an overall Dice
score of 0.626 for the error map prediction task, and observe a high Pearson
correlation coefficient (PCC) of 0.972 between QI and the actual segmentation
accuracy (Acc), as well as a low mean absolute error (MAE) of 0.0048 between
them, which evidences the efficacy of our method in both error map prediction
and quality assessment.Comment: 9 pages, accepted by MICCAI'1
Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease
We propose a new iterative segmentation model which can be accurately learned
from a small dataset. A common approach is to train a model to directly segment
an image, requiring a large collection of manually annotated images to capture
the anatomical variability in a cohort. In contrast, we develop a segmentation
model that recursively evolves a segmentation in several steps, and implement
it as a recurrent neural network. We learn model parameters by optimizing the
interme- diate steps of the evolution in addition to the final segmentation. To
this end, we train our segmentation propagation model by presenting incom-
plete and/or inaccurate input segmentations paired with a recommended next
step. Our work aims to alleviate challenges in segmenting heart structures from
cardiac MRI for patients with congenital heart disease (CHD), which encompasses
a range of morphological deformations and topological changes. We demonstrate
the advantages of this approach on a dataset of 20 images from CHD patients,
learning a model that accurately segments individual heart chambers and great
vessels. Com- pared to direct segmentation, the iterative method yields more
accurate segmentation for patients with the most severe CHD malformations.Comment: Presented at the Deep Learning in Medical Image Analysis Workshop,
MICCAI 201
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