Multi-template matching: a versatile tool for object-localization in microscopy images.
Laurent S V ThomasJochen GehrigPublished in: BMC bioinformatics (2020)
The novel multi-template matching is a simple yet powerful object-localization algorithm, that requires no data-pre-processing or annotation. Our implementation can be used out-of-the-box by non-expert users for any type of 2D-image. It is compatible with a large variety of applications including, for instance, analysis of large-scale datasets originating from automated microscopy, detection and tracking of objects in time-lapse assays, or as a general image-analysis step in any custom processing pipelines. Using different templates corresponding to distinct object categories, the tool can also be used for classification of the detected regions.
Keyphrases
- deep learning
- working memory
- high throughput
- label free
- machine learning
- single molecule
- convolutional neural network
- artificial intelligence
- high resolution
- optical coherence tomography
- high speed
- rna seq
- molecularly imprinted
- big data
- primary care
- healthcare
- single cell
- electronic health record
- binding protein
- quality improvement
- clinical practice