Machine learning as a tool for classifying electron tomographic reconstructions

Staniewicz, Lech; Midgley, Paul A.

doi:10.1186/s40679-015-0010-x

Cited by 23 publications

(14 citation statements)

References 28 publications

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“…The modularity as a FIJI plugin has meant that it has found widespread use amongst many types of imaging data, even tomography where substantial amounts of data can be discarded (e.g. one in 15 images are used) (Staniewicz and Midgley 2015). It has also meant that there is now widespread deployment of classifiers based on an ML toolkit that microscopists or clinicians use to augment their throughput when classifying images for diagnosis.…”

Section: Segmentationmentioning

confidence: 99%

Machine learning: applications of artificial intelligence to imaging and diagnosis

2018

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Machine learning (ML) is a form of artificial intelligence which is placed to transform the twenty-first century. Rapid, recent progress in its underlying architecture and algorithms and growth in the size of datasets have led to increasing computer competence across a range of fields. These include driving a vehicle, language translation, chatbots and beyond human performance at complex board games such as Go. Here, we review the fundamentals and algorithms behind machine learning and highlight specific approaches to learning and optimisation. We then summarise the applications of ML to medicine. In particular, we showcase recent diagnostic performances, and caveats, in the fields of dermatology, radiology, pathology and general microscopy.

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Section: Segmentationmentioning

confidence: 99%

Machine learning: applications of artificial intelligence to imaging and diagnosis

2018

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“…A recent example denoises low dose SEM images by removing the additive white Gaussian noise (from the detector electronics) and the underlying Poisson-Gaussian noise of the image using patch-based algorithms 62 . Another report 63 uses non-linear anisotropic diffusion as part of a machine learning scheme to denoise images for electron tomography.…”

Section: Advanced Metrology Techniquesmentioning

confidence: 99%

Metrology for the next generation of semiconductor devices

Badaroglu²,

et al. 2018

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The semiconductor industry continues to produce ever smaller devices that are ever more complex in shape and contain ever more types of materials. The ultimate sizes and functionality of these new devices will be affected by fundamental and engineering limits such as heat dissipation, carrier mobility and fault tolerance thresholds. At present, it is unclear which are the best measurement methods needed to evaluate the nanometre-scale features of such devices and how the fundamental limits will affect the required metrology. Here, we review state-of-the-art dimensional metrology methods for integrated circuits, considering the advantages, limitations and potential improvements of the various approaches. We describe how integrated circuit device design and industry requirements will affect lithography options and consequently metrology requirements. We also discuss potentially powerful emerging technologies and highlight measurement problems that at present have no obvious solution.

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“…The close packing of the curves might have negated the need to exclusively remove structures with a clear orientation. Therefore, it is wise to use the function f in eqn (19) in the proposed transform for this test image. The enhanced curves and the subsequent segmentation result with hard thresholding are shown in Fig.…”

Section: B Performance In 3dmentioning

confidence: 99%

3D Orientation Field Transform

Yeung,

Cai,

Liang

et al. 2020

Preprint

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The two-dimensional (2D) orientation field transform has been proved to be effective at enhancing 2D contours and curves in images by means of top-down processing. It, however, has no counterpart in three-dimensional (3D) images due to the extremely complicated orientation in 3D compared to 2D. Practically and theoretically, the demand and interest in 3D can only be increasing. In this work, we modularise the concept and generalise it to 3D curves. Different modular combinations are found to enhance curves to different extents and with different sensitivity to the packing of the 3D curves. In principle, the proposed 3D orientation field transform can naturally tackle any dimensions. As a special case, it is also ideal for 2D images, owning simpler methodology compared to the previous 2D orientation field transform. The proposed method is demonstrated with several transmission electron microscopy tomograms ranging from 2D curve enhancement to, the more important and interesting, 3D ones.

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Machine learning as a tool for classifying electron tomographic reconstructions

Cited by 23 publications

References 28 publications

Machine learning: applications of artificial intelligence to imaging and diagnosis

Machine learning: applications of artificial intelligence to imaging and diagnosis

Metrology for the next generation of semiconductor devices

3D Orientation Field Transform

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