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Lung cancer is one of the deadliest cancers, and in part its effectivediagnosis and treatment depend on the accurate delineation of the tumor.Human-centered segmentation, which is currently the most common approach, issubject to inter-observer variability, and is also time-consuming, consideringthe fact that only experts are capable of providing annotations. Automatic andsemi-automatic tumor segmentation methods have recently shown promisingresults. However, as different researchers have validated their algorithmsusing various datasets and performance metrics, reliably evaluating thesemethods is still an open challenge. The goal of the Lung-Originated TumorSegmentation from Computed Tomography Scan (LOTUS) Benchmark created through2018 IEEE Video and Image Processing (VIP) Cup competition, is to provide aunique dataset and pre-defined metrics, so that different researchers candevelop and evaluate their methods in a unified fashion. The 2018 VIP Cupstarted with a global engagement from 42 countries to access the competitiondata. At the registration stage, there were 129 members clustered into 28 teamsfrom 10 countries, out of which 9 teams made it to the final stage and 6 teamssuccessfully completed all the required tasks. In a nutshell, all thealgorithms proposed during the competition, are based on deep learning modelscombined with a false positive reduction technique. Methods developed by thethree finalists show promising results in tumor segmentation, however, moreeffort should be put into reducing the false positive rate. This competitionmanuscript presents an overview of the VIP-Cup challenge, along with theproposed algorithms and results.

Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark