Fast Barcode Retrieval for Consensus Contouring
H.R.Tizhoosh, G.J.Czarnota

TL;DR
This paper introduces a fast barcode-based retrieval method to efficiently access large image atlases for consensus contouring in medical imaging, improving accuracy and computational efficiency.
Contribution
It presents a novel content-based barcode approach for rapid retrieval of similar cases, enabling large atlas use in consensus segmentation.
Findings
Achieved 8% average error on synthetic prostate images.
Attained 87% Jaccard overlap with expert contours on real MRI data.
Demonstrated reliable case retrieval with small datasets.
Abstract
Marking tumors and organs is a challenging task suffering from both inter- and intra-observer variability. The literature quantifies observer variability by generating consensus among multiple experts when they mark the same image. Automatically building consensus contours to establish quality assurance for image segmentation is presently absent in the clinical practice. As the \emph{big data} becomes more and more available, techniques to access a large number of existing segments of multiple experts becomes possible. Fast algorithms are, hence, required to facilitate the search for similar cases. The present work puts forward a potential framework that tested with small datasets (both synthetic and real images) displays the reliability of finding similar images. In this paper, the idea of content-based barcodes is used to retrieve similar cases in order to build consensus contours in…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Medical Image Segmentation Techniques · Advanced Neural Network Applications
