94 research outputs found
A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation
In this paper, we adopt 3D Convolutional Neural Networks to segment
volumetric medical images. Although deep neural networks have been proven to be
very effective on many 2D vision tasks, it is still challenging to apply them
to 3D tasks due to the limited amount of annotated 3D data and limited
computational resources. We propose a novel 3D-based coarse-to-fine framework
to effectively and efficiently tackle these challenges. The proposed 3D-based
framework outperforms the 2D counterpart to a large margin since it can
leverage the rich spatial infor- mation along all three axes. We conduct
experiments on two datasets which include healthy and pathological pancreases
respectively, and achieve the current state-of-the-art in terms of
Dice-S{\o}rensen Coefficient (DSC). On the NIH pancreas segmentation dataset,
we outperform the previous best by an average of over 2%, and the worst case is
improved by 7% to reach almost 70%, which indicates the reliability of our
framework in clinical applications.Comment: 9 pages, 4 figures, Accepted to 3D
Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation
We aim at segmenting small organs (e.g., the pancreas) from abdominal CT
scans. As the target often occupies a relatively small region in the input
image, deep neural networks can be easily confused by the complex and variable
background. To alleviate this, researchers proposed a coarse-to-fine approach,
which used prediction from the first (coarse) stage to indicate a smaller input
region for the second (fine) stage. Despite its effectiveness, this algorithm
dealt with two stages individually, which lacked optimizing a global energy
function, and limited its ability to incorporate multi-stage visual cues.
Missing contextual information led to unsatisfying convergence in iterations,
and that the fine stage sometimes produced even lower segmentation accuracy
than the coarse stage.
This paper presents a Recurrent Saliency Transformation Network. The key
innovation is a saliency transformation module, which repeatedly converts the
segmentation probability map from the previous iteration as spatial weights and
applies these weights to the current iteration. This brings us two-fold
benefits. In training, it allows joint optimization over the deep networks
dealing with different input scales. In testing, it propagates multi-stage
visual information throughout iterations to improve segmentation accuracy.
Experiments in the NIH pancreas segmentation dataset demonstrate the
state-of-the-art accuracy, which outperforms the previous best by an average of
over 2%. Much higher accuracies are also reported on several small organs in a
larger dataset collected by ourselves. In addition, our approach enjoys better
convergence properties, making it more efficient and reliable in practice.Comment: Accepted to CVPR 2018 (10 pages, 6 figures
Deep learning in medical imaging and radiation therapy
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd
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
EMIT-Diff: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model
Large-scale, big-variant, and high-quality data are crucial for developing
robust and successful deep-learning models for medical applications since they
potentially enable better generalization performance and avoid overfitting.
However, the scarcity of high-quality labeled data always presents significant
challenges. This paper proposes a novel approach to address this challenge by
developing controllable diffusion models for medical image synthesis, called
EMIT-Diff. We leverage recent diffusion probabilistic models to generate
realistic and diverse synthetic medical image data that preserve the essential
characteristics of the original medical images by incorporating edge
information of objects to guide the synthesis process. In our approach, we
ensure that the synthesized samples adhere to medically relevant constraints
and preserve the underlying structure of imaging data. Due to the random
sampling process by the diffusion model, we can generate an arbitrary number of
synthetic images with diverse appearances. To validate the effectiveness of our
proposed method, we conduct an extensive set of medical image segmentation
experiments on multiple datasets, including Ultrasound breast (+13.87%), CT
spleen (+0.38%), and MRI prostate (+7.78%), achieving significant improvements
over the baseline segmentation methods. For the first time, to our best
knowledge, the promising results demonstrate the effectiveness of our EMIT-Diff
for medical image segmentation tasks and show the feasibility of introducing a
first-ever text-guided diffusion model for general medical image segmentation
tasks. With carefully designed ablation experiments, we investigate the
influence of various data augmentation ratios, hyper-parameter settings, patch
size for generating random merging mask settings, and combined influence with
different network architectures.Comment: 15 page
Applications of artificial intelligence to prostate multiparametric MRI (mpMRI): Current and emerging trends
Prostate carcinoma is one of the most prevalent cancers worldwide. Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification. Machine learning (ML), a branch of artificial intelligence, can rapidly and accurately analyze mpMRI images. ML could provide better standardization and consistency in identifying prostate lesions and enhance prostate carcinoma management. This review summarizes ML applications to prostate mpMRI and focuses on prostate organ segmentation, lesion detection and segmentation, and lesion characterization. A literature search was conducted to find studies that have applied ML methods to prostate mpMRI. To date, prostate organ segmentation and volume approximation have been well executed using various ML techniques. Prostate lesion detection and segmentation are much more challenging tasks for ML and were attempted in several studies. They largely remain unsolved problems due to data scarcity and the limitations of current ML algorithms. By contrast, prostate lesion characterization has been successfully completed in several studies because of better data availability. Overall, ML is well situated to become a tool that enhances radiologists\u27 accuracy and speed
DEVELOPING MEDICAL IMAGE SEGMENTATION AND COMPUTER-AIDED DIAGNOSIS SYSTEMS USING DEEP NEURAL NETWORKS
Diagnostic medical imaging is an important non-invasive tool in medicine. It provides doctors (i.e., radiologists) with rich diagnostic information in clinical practice. Computer-aided diagnosis (CAD) schemes aim to provide a tool to assist the doctors for reading and interpreting medical images. Traditional CAD schemes are based on hand-crafted features and shallow supervised learning algorithms. They are greatly limited by the difficulties of accurate region segmentation and effective feature extraction. In this dissertation, our motivation is to apply deep learning techniques to address these challenges. We comprehensively investigated the feasibilities of applying deep learning technique to develop medical image segmentation and computer-aided diagnosis schemes for different imaging modalities and different tasks. First, we applied a two-step convolutional neural network architecture for selection of abdomen part and segmentation of subtypes of adipose tissue from abdominal CT images. We demonstrated high agreement between the segmentation generated by human and by our proposed deep learning models. Second, we explored to combine transfer learning technique with traditional hand-crafted features to improve the accuracy of breast mass classification from digital mammograms. Our results show that the ensemble of hand-crafted features and transferred features yields improvement of prediction performances. Third, we proposed a 3D fully convolutional network architecture with a novel coarse-to-fine residual module for prostate segmentation from MRI. State-of-art segmentation accuracy was obtained by using this model. We also investigated the feasibilities of applying fully convolutional network for prostate cancer detection based on multi-parametric MRI and obtained promising detection accuracy. Last, we proposed a novel cascaded neural network architecture with post-processing steps for nuclear segmentation from histology images. Superiority of the model was demonstrated by experiments. In summary, these study results demonstrated that deep learning is a very promising technology to help significantly improve efficacy of developing computer-aided diagnosis schemes of medical images and achieve higher performance
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