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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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- Lindblad, Joakim, et al.
(författare)
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High-resolution reconstruction by feature distance minimization from multiple views of an object
- 2015
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Ingår i: Proc. 5th International Conference on Image Processing Theory, Tools and Applications. - Piscataway, NJ : IEEE. - 9781479986361 ; , s. 29-34
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Konferensbidrag (refereegranskat)abstract
- We present a method which utilizes advantages of fuzzy object representations and image processing techniques adjusted to them, to further increase efficient utilization of image information. Starting from a number of low-resolution images of affine transformations of an object, we create its suitably defuzzified high-resolution reconstruction. We evaluate the proposed method on synthetic data, observing its performance w.r.t. noise sensitivity, influence of the number of used low-resolution images, sensitivity to object variation and to inaccurate registration. Our aim is to explore applicability of the method to real image data acquired by Transmission Electron Microscopy, in a biomedical application we are currently working on.
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- Sintorn, Ida-Maria, 1976-, et al.
(författare)
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Facilitating Ultrastructural Pathology through Automated Imaging and Analysis
- 2019
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Ingår i: Journal of Pathology Informatics. - : Elsevier. - 2229-5089 .- 2153-3539. ; 10:1, s. 38-39
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Transmission electron microscopy (TEM) is an important diagnostic tool for analyzing human tissue at the nm scale. It is the only option, or gold standard, for diagnosing several disorders e.g. cilia and renal diseases, rare cancers etc. However, conventional TEM microscopes are highly manual, technically complex and a special environment is required to house the bulky and sensitive machines. Interpretation of information is subjective, time consuming, and relies on a high level of expertise which, unfortunately, is rare for this specialty within pathology. Here, we present methods and results from an ongoing project with the goal to develop a smart and easy to use platform for ultrastructural pathologic diagnoses. The platform is based on the recently developed MiniTEM instrument, a highly automated table-top TEM. In the project we develop image analysis methods for guided as well as fully automated search and analysis of structures of interest. In addition we enrich MiniTEM with an integrated database for convenient image handling and traceability. These points are identified by user representatives as crucial for creating a cost-effective diagnostic platform. We will show strategies and results for using image analysis and machine learning for automated search for objects/regions of interest at low magnification as well as combining multiple object instances acquired at high magnification to enhance nm details necessary for correct diagnosis. This will be exemplified for diagnosing primary cilia dyskinesia and renal disorders. The automation in imaging and analysis within the platform is a big step towards digital ultrapathology.
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- Suveer, Amit, et al.
(författare)
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Automated detection of cilia in low magnification transmission electron microscopy images using template matching
- 2016
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Ingår i: Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on. - : IEEE. - 9781479923496 - 9781479923502 ; , s. 386-390
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Ultrastructural analysis using Transmission Electron Microscopy (TEM) is a common approach for diagnosing primary ciliary dyskinesia. The manually performed diagnostic procedure is time consuming and subjective, and automation of the process is highly desirable. We aim at automating the search for plausible cilia instances in images at low magnification, followed by acquisition of high magnification images of regions with detected cilia for further analysis. This paper presents a template matching based method for automated detection of cilia objects in low magnification TEM images, where object radii do not exceed 10 pixels. We evaluate the performance of a series of synthetic templates generated for this purpose by comparing automated detection with results manually created by an expert pathologist. The best template achieves a detection at equal error rate of 47% which suffices to identify densely populated cilia regions suitable for high magnification imaging.
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