A vocabulary for the identification and delineation of teratoma tissue components in hematoxylin and eosin-stained samples

The increasing availability of digital whole slide images opens new perspectives for computer-assisted image analysis complementing modern histopathology, assuming we can implement reliable and efficient image analysis algorithms to extract the biologically relevant information. Both validation and supervised learning techniques typically rely on ground truths manually made by human experts. However, this task is difficult, subjective and usually not exhaustive. This is a well-known issue in the field of biomedical imaging, and a common solution is the use of artificial "phantoms". Following this trend, we study the feasibility of synthesizing artificial histological images to create perfect ground truths. In this paper, we show that it is possible to generate a synthetic whole slide image with reasonable computing resources, and we propose a way to evaluate its quality.

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A vocabulary for the identification and delineation of teratoma tissue components in hematoxylin and eosin-stained samples

Cited by 2 publications

References 19 publications

Predictive sparse morphometric context for classification of histology sections

Predictive sparse morphometric context for classification of histology sections

Synthesizing whole slide images

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