| 1. |
- Ranefall, Petter, 1968-, et al.
(författare)
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Automatic grading of breast cancer from whole slide images of Ki67 stained tissue sections
- 2016
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- AimThis work describes a proof-of-principle study within the Exchange of Diagnostic Images in Networks (ExDIN) project, for automatic grading of breast cancer from whole slide images of Ki67 stained tissue sections. The idea was to mimic the manual grading process: “The assessment is carried out on invasive cancer within the area with the highest number of Ki67-positive cancer cell nuclei/area (hot spot), containing at least 200 cells.”MethodColor deconvolution to separate the image into brown and blue channels.Extract the 10 subsampled tiles (size corresponding to ~200 cells) with the highest values for pre-defined texture and color features.Analyze these tiles in full resolution and compute the maximum positivity (defined as area of positive cells in relation to total cell area, rather than number of cells, since that will speed up the computations and avoid introducing errors due to over- or under segmentation of connected objects). Figure 1. Illustration of the procedure. Hot spot candidates are extracted from low resolution tiles. Then the final hot spot is selected among the corresponding full resolution versions.The results show good correlation to manual estimates and the procedure takes ~4 minutes/slide.Future improvementsRules and features defined using machine learning based on training samples given by pathologists.User interface where suggested regions can be deselected manually.
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| 2. |
- Ranefall, Petter, 1968-, et al.
(författare)
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Fast Adaptive Local Thresholding Based on Ellipse fit
- 2016
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Konferensbidrag (refereegranskat)abstract
- In this paper we propose an adaptive thresholding method where each object is thresholded optimizing its shape. The method is based on a component tree representation, which can be computed in quasi-linear time. We test and evaluate the method on images of bacteria from three different live-cell analysis experiments and show that the proposed method produces segmentation results comparable to state-of-the-art but at least an order of magnitude faster. The method can be extended to compute any feature measurements that can be calculated in a cumulative way, and holds great potential for applications where a priori information on expected object size and shape is available.
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| 3. |
- Ranefall, Petter, 1968-, et al.
(författare)
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Global And Local Adaptive Gray-level Thresholding Based on Object Features
- 2016
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Ingår i: Swedish Symposium on Image Analysis 2016.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In this paper we propose a) a fast and robustglobal gray-level thresholding method based on object size,where the selection of threshold level is based on recall andmaximum precision with regard to objects within a givensize interval, and b) an adaptive thresholding method whereeach object is thresholded optimizing its shape. The methodsare based on on the component tree representation, whichcan be computed in quasi-linear time. We show that forreal images of cell nuclei and synthetic data sets mimickingfluorescent spots the proposed methods are more robust thanall standard global thresholding methods in ImageJ andCellProfiler. The methods can be extended to compute anyfeature measurements that can be calculated in a cumulativeway, and hold great potential for applications where a prioriinformation on expected object size and shape is available.
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| 4. |
- Ranefall, Petter, 1968-, et al.
(författare)
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Global Gray-level Thresholding Based on Object Size
- 2016
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Ingår i: Cytometry Part A. - : John Wiley & Sons. - 1552-4922 .- 1552-4930. ; 89A:4, s. 385-390
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Tidskriftsartikel (refereegranskat)abstract
- In this article, we propose a fast and robust global gray-level thresholding method based on object size, where the selection of threshold level is based on recall and maximum precision with regard to objects within a given size interval. The method relies on the component tree representation, which can be computed in quasi-linear time. Feature-based segmentation is especially suitable for biomedical microscopy applications where objects often vary in number, but have limited variation in size. We show that for real images of cell nuclei and synthetic data sets mimicking fluorescent spots the proposed method is more robust than all standard global thresholding methods available for microscopy applications in ImageJ and CellProfiler. The proposed method, provided as ImageJ and CellProfiler plugins, is simple to use and the only required input is an interval of the expected object sizes.
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| 5. |
- Ranefall, Petter, 1968-, et al.
(författare)
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Your New Default Thresholding Method? : A robust global gray-level thresholding method based on object features
- 2015
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Ingår i: BioImage Informatics Conference 2015.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- We present a new robust method for global gray-level thresholding. The method is based on object features and the input is an interval of the expected object sizes. It is especially suitable for biomedical microscopy applications where objects often vary in number, but have limited variation in size. Our vision is that this method should be the first thresholding method you try when designing a pipeline for object segmentation, and thus your new default method.
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