ArticleExperimental IR meets multilinguality, multimodality, and interaction : 8th International Conference of the CLEF Association, CLEF 2017, Dublin, Ireland, September 11-14, 2017, Proceedings. Cross-Language Evaluation Forum. Conference2017
Segmenting Compound Biomedical Figures into Their Constituent Panels.
Article in Experimental IR meets multilinguality, multimodality, and interaction : 8th International Conference of the CLEF Association, CLEF 2017, Dublin, Ireland, September 11-14, 2017, Proceedings. Cross-Language Evaluation Forum. Conference, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Article
- A hybrid multi-panel image segmentation framework for improved medical image retrieval system.PloS one · 2025Article
- An automatic system for extracting figure-caption pair from medical documents: a six-fold approach.PeerJ. Computer science · 2023Article
- Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised Learning.The journal of machine learning for biomedical imaging · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Many of the figures in biomedical publications are compound figures consisting of multiple panels. Segmenting such figures into constituent panels is an essential first step for harvesting the visual information within the biomedical documents. Current figure separation methods are based primarily on gap-detection and suffer from over- and under-segmentation. In this paper, we propose a new compound figure segmentation scheme based on Connected Component Analysis. To overcome shortcomings typically manifested by existing methods, we develop a quality assessment step for evaluating and modifying segmentations. Two methods are proposed to re-segment the images if the initial segmentations are inaccurate. Experiments and results comparing the performance of our method to that of other top methods demonstrate the effectiveness of our approach.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.