637 research outputs found
MDS-Net: A Model-Driven Stack-Based Fully Convolutional Network for Pancreas Segmentation
The irregular geometry and high inter-slice variability in computerized
tomography (CT) scans of the human pancreas make an accurate segmentation of
this crucial organ a challenging task for existing data-driven deep learning
methods. To address this problem, we present a novel model-driven stack-based
fully convolutional network with a bi-directional convolutional long short-term
memory network for pancreas segmentation, termed MDS-Net. The MDS-Net's cost
function includes data approximation term and prior knowledge regularization
term combined with a stack scheme for capturing and fusing the two-dimensional
(2D) and local three-dimensional (3D) context information. Specifically, 3D CT
scans are divided into multiple stacks, and each multi-slice stack is used as a
basic unit for network training and modeling of the local spatial context. To
highlight the importance of single slices in segmentation, the inter-slice
relationships in the stack data are also incorporated in the MDS-Net framework.
For implementing this proposed model-driven method, we create a stack-based
U-Net architecture and successfully derive its back-propagation procedure for
end-to-end training. Furthermore, a bi-directional convolutional long
short-term memory (BiCLSTM) network is utilized to integrate upper and lower
slice information, thereby ensuring the consistency of adjacent CT slices and
intra-stack. Finally, extensive quantitative assessments on the NIH Pancreas-CT
dataset demonstrated higher pancreatic segmentation accuracy and reliability of
MDS-Net compared to other state-of-the-art methods
A deep level set method for image segmentation
This paper proposes a novel image segmentation approachthat integrates fully
convolutional networks (FCNs) with a level setmodel. Compared with a FCN, the
integrated method can incorporatesmoothing and prior information to achieve an
accurate segmentation.Furthermore, different than using the level set model as
a post-processingtool, we integrate it into the training phase to fine-tune the
FCN. Thisallows the use of unlabeled data during training in a
semi-supervisedsetting. Using two types of medical imaging data (liver CT and
left ven-tricle MRI data), we show that the integrated method achieves
goodperformance even when little training data is available, outperformingthe
FCN or the level set model alone
Isointense Infant Brain Segmentation with a Hyper-dense Connected Convolutional Neural Network
Neonatal brain segmentation in magnetic resonance (MR) is a challenging
problem due to poor image quality and low contrast between white and gray
matter regions. Most existing approaches for this problem are based on
multi-atlas label fusion strategies, which are time-consuming and sensitive to
registration errors. As alternative to these methods, we propose a
hyper-densely connected 3D convolutional neural network that employs MR-T1 and
T2 images as input, which are processed independently in two separated paths.
An important difference with previous densely connected networks is the use of
direct connections between layers from the same and different paths. Adopting
such dense connectivity helps the learning process by including deep
supervision and improving gradient flow. We evaluated our approach on data from
the MICCAI Grand Challenge on 6-month infant Brain MRI Segmentation (iSEG),
obtaining very competitive results. Among 21 teams, our approach ranked first
or second in most metrics, translating into a state-of-the-art performance.Comment: Oral presentation at ISBI 2018. The last version of the paper is
updated with the reference of the iSEG comparative study, published in 2019
at IEEE TM
DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy
We introduce DeepNAT, a 3D Deep convolutional neural network for the
automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance
images. DeepNAT is an end-to-end learning-based approach to brain segmentation
that jointly learns an abstract feature representation and a multi-class
classification. We propose a 3D patch-based approach, where we do not only
predict the center voxel of the patch but also neighbors, which is formulated
as multi-task learning. To address a class imbalance problem, we arrange two
networks hierarchically, where the first one separates foreground from
background, and the second one identifies 25 brain structures on the
foreground. Since patches lack spatial context, we augment them with
coordinates. To this end, we introduce a novel intrinsic parameterization of
the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As
network architecture, we use three convolutional layers with pooling, batch
normalization, and non-linearities, followed by fully connected layers with
dropout. The final segmentation is inferred from the probabilistic output of
the network with a 3D fully connected conditional random field, which ensures
label agreement between close voxels. The roughly 2.7 million parameters in the
network are learned with stochastic gradient descent. Our results show that
DeepNAT compares favorably to state-of-the-art methods. Finally, the purely
learning-based method may have a high potential for the adaptation to young,
old, or diseased brains by fine-tuning the pre-trained network with a small
training sample on the target application, where the availability of larger
datasets with manual annotations may boost the overall segmentation accuracy in
the future.Comment: Accepted for publication in NeuroImage, special issue "Brain
Segmentation and Parcellation", 201
Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image
Recently, more and more attention is drawn to the field of medical image
synthesis across modalities. Among them, the synthesis of computed tomography
(CT) image from T1-weighted magnetic resonance (MR) image is of great
importance, although the mapping between them is highly complex due to large
gaps of appearances of the two modalities. In this work, we aim to tackle this
MR-to-CT synthesis by a novel deep embedding convolutional neural network
(DECNN). Specifically, we generate the feature maps from MR images, and then
transform these feature maps forward through convolutional layers in the
network. We can further compute a tentative CT synthesis from the midway of the
flow of feature maps, and then embed this tentative CT synthesis back to the
feature maps. This embedding operation results in better feature maps, which
are further transformed forward in DECNN. After repeat-ing this embedding
procedure for several times in the network, we can eventually synthesize a
final CT image in the end of the DECNN. We have validated our proposed method
on both brain and prostate datasets, by also compar-ing with the
state-of-the-art methods. Experimental results suggest that our DECNN (with
repeated embedding op-erations) demonstrates its superior performances, in
terms of both the perceptive quality of the synthesized CT image and the
run-time cost for synthesizing a CT image
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Liver segmentation using 3D CT scans.
Master of Science in Computer Science. University of KwaZulu-Natal, Durban, 2018.Abstract available in PDF file
A Cross-Stitch Architecture for Joint Registration and Segmentation in Adaptive Radiotherapy
Recently, joint registration and segmentation has been formulated in a deep
learning setting, by the definition of joint loss functions. In this work, we
investigate joining these tasks at the architectural level. We propose a
registration network that integrates segmentation propagation between images,
and a segmentation network to predict the segmentation directly. These networks
are connected into a single joint architecture via so-called cross-stitch
units, allowing information to be exchanged between the tasks in a learnable
manner. The proposed method is evaluated in the context of adaptive
image-guided radiotherapy, using daily prostate CT imaging. Two datasets from
different institutes and manufacturers were involved in the study. The first
dataset was used for training (12 patients) and validation (6 patients), while
the second dataset was used as an independent test set (14 patients). In terms
of mean surface distance, our approach achieved mm, mm, mm, and mm on the validation set and
mm, mm, mm, and mm
on the test set for the prostate, bladder, seminal vesicles, and rectum,
respectively. The proposed multi-task network outperformed single-task
networks, as well as a network only joined through the loss function, thus
demonstrating the capability to leverage the individual strengths of the
segmentation and registration tasks. The obtained performance as well as the
inference speed make this a promising candidate for daily re-contouring in
adaptive radiotherapy, potentially reducing treatment-related side effects and
improving quality-of-life after treatment.Comment: Accepted to MIDL 202
Deep Geodesic Learning for Segmentation and Anatomical Landmarking
In this paper, we propose a novel deep learning framework for anatomy
segmentation and automatic landmark- ing. Specifically, we focus on the
challenging problem of mandible segmentation from cone-beam computed tomography
(CBCT) scans and identification of 9 anatomical landmarks of the mandible on
the geodesic space. The overall approach employs three inter-related steps. In
step 1, we propose a deep neu- ral network architecture with carefully designed
regularization, and network hyper-parameters to perform image segmentation
without the need for data augmentation and complex post- processing refinement.
In step 2, we formulate the landmark localization problem directly on the
geodesic space for sparsely- spaced anatomical landmarks. In step 3, we propose
to use a long short-term memory (LSTM) network to identify closely- spaced
landmarks, which is rather difficult to obtain using other standard detection
networks. The proposed fully automated method showed superior efficacy compared
to the state-of-the- art mandible segmentation and landmarking approaches in
craniofacial anomalies and diseased states. We used a very challenging CBCT
dataset of 50 patients with a high-degree of craniomaxillofacial (CMF)
variability that is realistic in clinical practice. Complementary to the
quantitative analysis, the qualitative visual inspection was conducted for
distinct CBCT scans from 250 patients with high anatomical variability. We have
also shown feasibility of the proposed work in an independent dataset from
MICCAI Head-Neck Challenge (2015) achieving the state-of-the-art performance.
Lastly, we present an in-depth analysis of the proposed deep networks with
respect to the choice of hyper-parameters such as pooling and activation
functions.Comment: 14 pages, 12 Figures, IEEE Transactions on Medical Imaging 2018,
TMI-2018-0898.R
Improving nuclear medicine with deep learning and explainability: two real-world use cases in parkinsonian syndrome and safety dosimetry
Computer vision in the area of medical imaging has rapidly improved during recent years as a consequence of developments in deep learning and explainability algorithms. In addition, imaging in nuclear medicine is becoming increasingly sophisticated, with the emergence of targeted radiotherapies that enable treatment and imaging on a molecular level (“theranostics”) where radiolabeled targeted molecules are directly injected into the bloodstream. Based on our recent work, we present two use-cases in nuclear medicine as follows: first, the impact of automated organ segmentation required for personalized dosimetry in patients with neuroendocrine tumors and second, purely data-driven identification and verification of brain regions for diagnosis of Parkinson’s disease. Convolutional neural network was used for automated organ segmentation on computed tomography images. The segmented organs were used for calculation of the energy deposited into the organ-at-risk for patients treated with a radiopharmaceutical. Our method resulted in faster and cheaper dosimetry and only differed by 7% from dosimetry performed by two medical physicists. The identification of brain regions, however was analyzed on dopamine-transporter single positron emission tomography images using convolutional neural network and explainability, i.e., layer-wise relevance propagation algorithm. Our findings confirm that the extra-striatal brain regions, i.e., insula, amygdala, ventromedial prefrontal cortex, thalamus, anterior temporal cortex, superior frontal lobe, and pons contribute to the interpretation of images beyond the striatal regions. In current common diagnostic practice, however, only the striatum is the reference region, while extra-striatal regions are neglected. We further demonstrate that deep learning-based diagnosis combined with explainability algorithm can be recommended to support interpretation of this image modality in clinical routine for parkinsonian syndromes, with a total computation time of three seconds which is compatible with busy clinical workflow.
Overall, this thesis shows for the first time that deep learning with explainability can achieve results competitive with human performance and generate novel hypotheses, thus paving the way towards improved diagnosis and treatment in nuclear medicine
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