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- Gupta, Anindya, et al.
(författare)
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Convolutional neural networks for false positive reduction of automatically detected cilia in low magnification TEM images
- 2017
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Ingår i: Image Analysis. - Cham : Springer. - 9783319591254 ; , s. 407-418
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Konferensbidrag (refereegranskat)abstract
- Automated detection of cilia in low magnification transmission electron microscopy images is a central task in the quest to relieve the pathologists in the manual, time consuming and subjective diagnostic procedure. However, automation of the process, specifically in low magnification, is challenging due to the similar characteristics of non-cilia candidates. In this paper, a convolutional neural network classifier is proposed to further reduce the false positives detected by a previously presented template matching method. Adding the proposed convolutional neural network increases the area under Precision-Recall curve from 0.42 to 0.71, and significantly reduces the number of false positive objects.
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| 2. |
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| 3. |
- Lidayová, Kristína, et al.
(författare)
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Classification of cross-sections for vascular skeleton extraction using convolutional neural networks
- 2017
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Ingår i: 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017. - Cham : Springer. - 9783319609638 ; , s. 182-194
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Konferensbidrag (refereegranskat)abstract
- Recent advances in Computed Tomography Angiography provide high-resolution 3D images of the vessels. However, there is an inevitable requisite for automated and fast methods to process the increased amount of generated data. In this work, we propose a fast method for vascular skeleton extraction which can be combined with a segmentation algorithm to accelerate the vessel delineation. The algorithm detects central voxels - nodes - of potential vessel regions in the orthogonal CT slices and uses a convolutional neural network (CNN) to identify the true vessel nodes. The nodes are gradually linked together to generate an approximate vascular skeleton. The CNN classifier yields a precision of 0.81 and recall of 0.83 for the medium size vessels and produces a qualitatively evaluated enhanced representation of vascular skeletons.
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