Journal: Biophysical Reports

Article Title: ATLAS: Machine learning-enhanced filament analysis for the In Vitro Motility Assay

doi: 10.1016/j.bpr.2025.100221

Figure Lengend Snippet: Automated tracking of learned actin structures (ATLAS) workflow. ( A ) ATLAS receives stacks of motility images from a movie as input. ( B ) Each image is fed to the YOLOv5 ensemble, which identifies actin filaments by a detection bounding box ( green ). ( C ) Each individual detection is fed to the UNET segmentation model, which returns filament masks, which are in turn used to determine filament lengths (see for a comprehensive flowchart of the ATLAS routine). ( D ) Simultaneously, filament detections are sent to Deep SORT to assemble actin filament tracks. ( E ) Track data are then used to calculate root mean-square displacement (RMSD), and velocity is determined as the slope of the relationship between RMSD and time interval. ( F ) Sample images from the YOLOv5 and UNET training datasets, with filament contour annotations shown in yellow. ( G ) Branched filament detection due to filament crossings. For each pixel in the skeleton ( green ), the total number of neighboring pixels in the skeleton ( red ) is counted in both the adjacent eight-pixel neighborhood ( yellow ) and the outer 16-pixel neighborhood ( blue ). If either neighborhood has three or more occupied pixels from the filament skeleton ( red ), the entire filament detection is discarded as a “branched” filament. ( H ) Centroid correction. For filaments >2 μm in length, the centroid of the YOLOv5 filament detection ( green dot ) may lie far from the actual center of the filament skeleton ( blue dot ); therefore, the coordinates of the bounding box centroid are replaced with those of the filament center. ( I ) Illustration of Maximum Track Selection to ensure analysis of the maximum number of tracks that each arise from a distinct filament. Each track is identified by its start and stop. (∗) denotes the earliest frame containing the maximum number of tracks (in this case, seven). Tracks active in this frame are exclusively selected for analysis when MTS functionality is enabled.

Article Snippet: Consequently, approaches to automating actin filament motion analysis exist in the form of commercial (e.g., Imaris (Oxford Instruments, Abingdon, UK), Aivia (Leica microsystems, Deerfield, IL), and DiaTrack ( )) as well as custom solutions developed by academic groups ( , , , , , ).

Techniques: Selection

Journal: Biophysical Reports

Article Title: ATLAS: Machine learning-enhanced filament analysis for the In Vitro Motility Assay

doi: 10.1016/j.bpr.2025.100221

Figure Lengend Snippet: Filament tracking challenges and number of analyzable tracks. ( A ) Small filaments, moving at fast velocities, become untrackable as filament detection overlap ( red shaded area on left ) falls below cutoff values for Deep SORT. ( B ) Example of identity switching. Two filaments identified as ID 1 and ID 2 moving slowly between frames, t to t + 3. Their identities are switched when YOLOv5 detects the two filaments as a single object (frame t + 2). Resolution of this causes the Deep SORT track of the lower filament ( ID 2 ) to transition from the lower filament in frame t to the upper filament in frame t + 3 . In extremely slow-motion conditions, this introduces a large, incorrect displacement into this filament’s track, resulting in an inflated measured velocity. ( C ) Stacks of 2D heatmaps of average number of tracks per filament within each movie frame (left) and average number of tracks per filament in complete movie (right) under various conditions. ( D ) Example of the benefit of “Maximum Track Selection” (MTS) analysis of SAMY movies featuring 10 filaments per movie, moving at velocities ranging from 0.1 to 8 μm/s. Without MTS enabled (−MTS), the number of detected tracks increases with velocity, in spite of a constant number of filaments per frame. With MTS (+MTS), the number of tracks is consistent with the number of filaments and insensitive to velocity. Error bars denote standard deviation.

Article Snippet: Consequently, approaches to automating actin filament motion analysis exist in the form of commercial (e.g., Imaris (Oxford Instruments, Abingdon, UK), Aivia (Leica microsystems, Deerfield, IL), and DiaTrack ( )) as well as custom solutions developed by academic groups ( , , , , , ).

Techniques: Selection, Standard Deviation