326 research outputs found
End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation
Automatic segmentation of abdomen organs using medical imaging has many
potential applications in clinical workflows. Recently, the state-of-the-art
performance for organ segmentation has been achieved by deep learning models,
i.e., convolutional neural network (CNN). However, it is challenging to train
the conventional CNN-based segmentation models that aware of the shape and
topology of organs. In this work, we tackle this problem by introducing a novel
end-to-end shape learning architecture -- organ point-network. It takes deep
learning features as inputs and generates organ shape representations as points
that located on organ surface. We later present a novel adversarial shape
learning objective function to optimize the point-network to capture shape
information better. We train the point-network together with a CNN-based
segmentation model in a multi-task fashion so that the shared network
parameters can benefit from both shape learning and segmentation tasks. We
demonstrate our method with three challenging abdomen organs including liver,
spleen, and pancreas. The point-network generates surface points with
fine-grained details and it is found critical for improving organ segmentation.
Consequently, the deep segmentation model is improved by the introduced shape
learning as significantly better Dice scores are observed for spleen and
pancreas segmentation.Comment: Accepted to International Workshop on Machine Learning in Medical
Imaging (MLMI2019
Multi-modality Medical Image Segmentation with Unsupervised Domain Adaptation
Advances in medical imaging have greatly aided in providing accurate and fast medical diagnosis, followed by recent deep learning developments enabling the efficient and cost-effective analysis of medical images. Among different image processing tasks, medical segmentation is one of the most crucial aspects because it provides the class, location, size, and shape of the subject of interest, which is invaluable and essential for diagnostics. Nevertheless, acquiring annotations for training data usually requires expensive manpower and specialised expertise, making supervised training difficult. To overcome these problems, unsupervised domain adaptation (UDA) has been adopted to bridge knowledge between different domains. Despite the appearance dissimilarities of different modalities such as MRI and CT, researchers have concluded that structural features of the same anatomy are universal across modalities, which unfolded the study of multi-modality image segmentation with UDA methods.
The traditional UDA research tackled the domain shift problem by minimising the distance of the source and target distributions in latent spaces with the help of advanced mathematics. However, with the recent development of the generative adversarial network (GAN), the adversarial UDA methods have shown outstanding performance by producing synthetic images to mitigate the domain gap in training a segmentation network for the target domain. Most existing studies focus on modifying the network architecture, but few investigate the generative adversarial training strategy. Inspired by the recent success of state-of-the-art data augmentation techniques in classification tasks, we designed a novel mix-up strategy to assist GAN training for the better synthesis of structural details, consequently leading to better segmentation results.
In this thesis, we propose SynthMix, an add-on module with a natural yet effective training policy that can promote synthetic quality without altering the network architecture. SynthMix is a mix-up synthesis scheme designed for integration with the adversarial logic of GAN networks. Traditional GAN approaches judge an image as a whole which could be easily dominated by discriminative features, resulting in little improvement of delicate structures in synthesis. In contrast, SynthMix uses the data augmentation technique to reinforce detail transformation at local regions. Specifically, it coherently mixes up aligned images of real and synthetic samples at local regions to stimulate the generation of fine-grained features examined by an associated inspector for domain-specific details. We evaluated our method on two segmentation benchmarks among three publicly available datasets. Our method showed a significant performance gain compared with existing state-of-the-art approaches
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions
Medical Image Analysis is currently experiencing a paradigm shift due to Deep
Learning. This technology has recently attracted so much interest of the
Medical Imaging community that it led to a specialized conference in `Medical
Imaging with Deep Learning' in the year 2018. This article surveys the recent
developments in this direction, and provides a critical review of the related
major aspects. We organize the reviewed literature according to the underlying
Pattern Recognition tasks, and further sub-categorize it following a taxonomy
based on human anatomy. This article does not assume prior knowledge of Deep
Learning and makes a significant contribution in explaining the core Deep
Learning concepts to the non-experts in the Medical community. Unique to this
study is the Computer Vision/Machine Learning perspective taken on the advances
of Deep Learning in Medical Imaging. This enables us to single out `lack of
appropriately annotated large-scale datasets' as the core challenge (among
other challenges) in this research direction. We draw on the insights from the
sister research fields of Computer Vision, Pattern Recognition and Machine
Learning etc.; where the techniques of dealing with such challenges have
already matured, to provide promising directions for the Medical Imaging
community to fully harness Deep Learning in the future
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