Caption-based topical descriptors for microscopic images as published in academic papers.

With the advancement of biomedical imaging technologies, visual findings of scientific discoveries are increasingly available through published academic papers. As evident from most biomedical publishers, the visual findings in figures are stored separately in literature databases. However, the stored images are not easily retrievable because the images are not indexed with descriptors necessary for facilitating further retrieval. Compared to conventional indexing and abstracting services, which exist mostly for text-based information retrieval, very little attention has been given to the investigation of image retrieval in medicine. For instance, there is no way in the current PubMed system to retrieve breast cancer images from middle-aged women scanned from immunohistochemistry staining slides. One can only gain access to published images via hyperlink(s) within the main document. More importantly, the visual findings in the papers are usually described in accompanying texts, such as captions. Captions describing visual findings in the text often contain detailed experimental procedures and results corresponding to a specific image. If a reader wants to grasp core findings reported in images, captions should not be overlooked. However, accessibility is limited to search published images by word(s) in captions. One of the image-intensive biomedical disciplines is pathology. A growing number of digitized pathology images are used for clinical diagnosis, scientific research, and biomedical education. Currently, the digital archiving of scanned pathologic images allows for storage of images without the risk of breakage, fading, or scratching. However, the archived images are difficult to search due to inadequate descriptions. For instance, they are normally labeled only with a sequential file name or an abbreviated diagnostic name such as liver01.jpeg. Moreover, no comprehensive or standard vocabulary specific to image retrieval is applied to published articles in online literature databases such as PubMed Central. As the basic link between an image and the content of published work, captions can be the best source of topical descriptors for non-text information contained in a scholarly paper. However, only a limited number of studies have assessed topical descriptors extracted from captions attached to biomedical images. Of those studies, none has attempted to discover characteristics of keywords from microscopic image captions. As a preliminary step toward understanding the topical description of published microscopic images, the current study assessed the major characteristics of a set of topical descriptors from an automatic keyword finder and from human indexers based on selected captions downloaded from the PubMed Central database. In order to promote greater accessibility of published microscopic images, the current study therefore discussed major findings on core descriptors and their contextual mapping results into vocabularies in the Unified Medical Language System through the MetaMap Transfer Engine (MMTx) developed by the U.S. National Library of Medicine (NLM).