50 research outputs found
Segmentation-by-Detection: A Cascade Network for Volumetric Medical Image Segmentation
We propose an attention mechanism for 3D medical image segmentation. The
method, named segmentation-by-detection, is a cascade of a detection module
followed by a segmentation module. The detection module enables a region of
interest to come to attention and produces a set of object region candidates
which are further used as an attention model. Rather than dealing with the
entire volume, the segmentation module distills the information from the
potential region. This scheme is an efficient solution for volumetric data as
it reduces the influence of the surrounding noise which is especially important
for medical data with low signal-to-noise ratio. Experimental results on 3D
ultrasound data of the femoral head shows superiority of the proposed method
when compared with a standard fully convolutional network like the U-Net
End-to-end detection-segmentation network with ROI convolution
We propose an end-to-end neural network that improves the segmentation
accuracy of fully convolutional networks by incorporating a localization unit.
This network performs object localization first, which is then used as a cue to
guide the training of the segmentation network. We test the proposed method on
a segmentation task of small objects on a clinical dataset of ultrasound
images. We show that by jointly learning for detection and segmentation, the
proposed network is able to improve the segmentation accuracy compared to only
learning for segmentation. Code is publicly available at
https://github.com/vincentzhang/roi-fcn.Comment: ISBI 201
Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study
The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.Rudyanto, RD.; Kerkstra, S.; Van Rikxoort, EM.; Fetita, C.; Brillet, P.; Lefevre, C.; Xue, W.... (2014). Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study. Medical Image Analysis. 18(7):1217-1232. doi:10.1016/j.media.2014.07.003S1217123218
Sparse Classification for Significant Anatomy Detection in a Group Study
I will present a new framework for discriminative anatomy detection in high dimensional neuroimaging data. Current methods for identifying significant regions related to a group study typically use voxel-based mass univariate approaches. Those methods have limited ability to identify complex population differences because they do not take into account multivariate relationships in data. High dimensional pattern classification methods aim to optimally perform feature extraction and selection to find a set of features that differentiate the groups. However, they do not directly produce anatomically interpretable features. Following recent advances in sparse dimensionality reduction methods, we propose a sparse classification method that identifies anatomical regions that are both discriminative and clinically interpretable. Results on synthetic and real MRI data of multiple sclerosis patients and age- and gender-matched healthy controls show superior performance of our method in detecting stable and significant regions in a statistical group analysis when compared to a generative sparse method and to a voxel-based analysis method.Non UBCUnreviewedAuthor affiliation: University of AlbertaPostdoctora