To perform benchmarking, ground truth annotations should be encoded in a format that is specific to the associated problem class. BIA workflows are also expected to output results in the same format.
Currently 9 problem classes are supported in BIAFLOWS and their respective annotation formats and computed benchmark metrics are described below.
Note: each problem class has a long name (explicit) and short name (e.g. Object Segmentation / ObjSeg). The same hold for metrics (e.g. DICE / DC).
A description of each benchmark is available on the workflow runs result table by clicking on the symbol.
|Object Segmentation||Delineate objects or isolated regions||ObjSeg||Label masks||sample|
|Pixel/Voxel classification||Estimate pixels class||PixCla||Label masks||sample||
|Spot/Object Counting||Estimate the number of objects||SptCnt||Binary masks||sample||
|Spot/Object Detection||Detect objects in an image (e.g. nucleus)||ObjDet||Binary masks||sample|
|Filament Tree Tracing||Estimate the medial axis of a connected filament tree network (one per image)||TreTrc||SWC||sample SWC format||
|Filament Networks Tracing||Estimate the medial axis of one or several connected filament network(s)||LooTrc||Skeleton binary masks||sample||
|Landmark Detection||Estimate the position of specific feature points||LndDet||Label masks||sample||
|Particle Tracking||Estimate the tracks followed by particles (no division)||PrtTrk||Label masks||sample||
|Object Tracking||Estimate object tracks and segmentation masks (with possible divisions)||ObjTrk||Label masks + Division text file||sample|