3,882 research outputs found
Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning
Convolutional neural networks (CNNs) have achieved state-of-the-art
performance for automatic medical image segmentation. However, they have not
demonstrated sufficiently accurate and robust results for clinical use. In
addition, they are limited by the lack of image-specific adaptation and the
lack of generalizability to previously unseen object classes. To address these
problems, we propose a novel deep learning-based framework for interactive
segmentation by incorporating CNNs into a bounding box and scribble-based
segmentation pipeline. We propose image-specific fine-tuning to make a CNN
model adaptive to a specific test image, which can be either unsupervised
(without additional user interactions) or supervised (with additional
scribbles). We also propose a weighted loss function considering network and
interaction-based uncertainty for the fine-tuning. We applied this framework to
two applications: 2D segmentation of multiple organs from fetal MR slices,
where only two types of these organs were annotated for training; and 3D
segmentation of brain tumor core (excluding edema) and whole brain tumor
(including edema) from different MR sequences, where only tumor cores in one MR
sequence were annotated for training. Experimental results show that 1) our
model is more robust to segment previously unseen objects than state-of-the-art
CNNs; 2) image-specific fine-tuning with the proposed weighted loss function
significantly improves segmentation accuracy; and 3) our method leads to
accurate results with fewer user interactions and less user time than
traditional interactive segmentation methods.Comment: 11 pages, 11 figure
Finsler Active Contours
©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TPAMI.2007.70713In this paper, we propose an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. In the isotropic case, the euclidean metric is locally multiplied by a scalar conformal factor based on image information such that the weighted length of curves lying on points of interest (typically edges) is small. The conformal factor that is chosen depends only upon position and is in this sense isotropic. Although directional information has been studied previously for other segmentation frameworks, here, we show that if one desires to add directionality in the conformal active contour framework, then one gets a well-defined minimization problem in the case that the factor defines a Finsler metric. Optimal curves may be obtained using the calculus of variations or dynamic programming-based schemes. Finally, we demonstrate the technique by extracting roads from aerial imagery, blood vessels from medical angiograms, and neural tracts from diffusion-weighted magnetic resonance imagery
Beyond KernelBoost
In this Technical Report we propose a set of improvements with respect to the
KernelBoost classifier presented in [Becker et al., MICCAI 2013]. We start with
a scheme inspired by Auto-Context, but that is suitable in situations where the
lack of large training sets poses a potential problem of overfitting. The aim
is to capture the interactions between neighboring image pixels to better
regularize the boundaries of segmented regions. As in Auto-Context [Tu et al.,
PAMI 2009] the segmentation process is iterative and, at each iteration, the
segmentation results for the previous iterations are taken into account in
conjunction with the image itself. However, unlike in [Tu et al., PAMI 2009],
we organize our recursion so that the classifiers can progressively focus on
difficult-to-classify locations. This lets us exploit the power of the
decision-tree paradigm while avoiding over-fitting. In the context of this
architecture, KernelBoost represents a powerful building block due to its
ability to learn on the score maps coming from previous iterations. We first
introduce two important mechanisms to empower the KernelBoost classifier,
namely pooling and the clustering of positive samples based on the appearance
of the corresponding ground-truth. These operations significantly contribute to
increase the effectiveness of the system on biomedical images, where texture
plays a major role in the recognition of the different image components. We
then present some other techniques that can be easily integrated in the
KernelBoost framework to further improve the accuracy of the final
segmentation. We show extensive results on different medical image datasets,
including some multi-label tasks, on which our method is shown to outperform
state-of-the-art approaches. The resulting segmentations display high accuracy,
neat contours, and reduced noise
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