| 1. |
- Fejne, Frida, 1986, et al.
(författare)
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Multiatlas Segmentation Using Robust Feature-Based Registration
- 2017
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Ingår i: , Cloud-Based Benchmarking of Medical Image Analysis. - Cham : Springer International Publishing. - 9783319496429 ; , s. 203-218
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This paper presents a pipeline which uses a multiatlas approach for multiorgan segmentation in whole-body CT images. In order to obtain accurate registrations between the target and the atlas images, we develop an adapted feature-based method which uses organ-specific features. These features are learnt during an offline preprocessing step, and thus, the algorithm still benefits from the speed of feature-based registration methods. These feature sets are then used to obtain pairwise non-rigid transformations using RANSAC followed by a thin-plate spline refinement or NiftyReg. The fusion of the transferred atlas labels is performed using a random forest classifier, and finally, the segmentation is obtained using graph cuts with a Potts model as interaction term. Our pipeline was evaluated on 20 organs in 10 whole-body CT images at the VISCERAL Anatomy Challenge, in conjunction with the International Symposium on Biomedical Imaging, Brooklyn, New York, in April 2015. It performed best on majority of the organs, with respect to the Dice index.
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| 2. |
- Larsson, Måns, 1989, et al.
(författare)
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Max-margin learning of deep structured models for semantic segmentation
- 2017
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783319591285 ; 10270 LNCS, s. 28-40
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Konferensbidrag (refereegranskat)abstract
- During the last few years most work done on the task of image segmentation has been focused on deep learning and Convolutional Neural Networks (CNNs) in particular. CNNs are powerful for modeling complex connections between input and output data but lack the ability to directly model dependent output structures, for instance, enforcing properties such as smoothness and coherence. This drawback motivates the use of Conditional Random Fields (CRFs), widely applied as a post-processing step in semantic segmentation. In this paper, we propose a learning framework that jointly trains the parameters of a CNN paired with a CRF. For this, we develop theoretical tools making it possible to optimize a max-margin objective with back-propagation. The max-margin loss function gives the model good generalization capabilities. Thus, the method is especially suitable for applications where labelled data is limited, for example, medical applications. This generalization capability is reflected in our results where we are able to show good performance on two relatively small medical datasets. The method is also evaluated on a public benchmark (frequently used for semantic segmentation) yielding results competitive to state-of-the-art. Overall, we demonstrate that end-to-end max-margin training is preferred over piecewise training when combining a CNN with a CRF.
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