Year: 2026
Type: preprint
Abstract: SUMMARY Focal adhesions (FAs) and actin stress fibers (SFs) are mechanically coupled but often analyzed separately. We present FilAdh, a unified framework for FA instance segmentation, SF instance reconstruction, morphometry, and FA-anchorage-based SF classification. Across 57 held-out HeLa, SUIT-2, and MRC-5 cell line images, FilAdh achieved the highest FA Dice, intersection over union (IoU), and recall and the highest SF clDice and skeleton precision among benchmarked methods; paired t tests with Holm correction indicated significant gains over all compared methods for FA Dice, IoU, and recall, and over classical methods for SF centerline metrics, with no significant differences detected relative to the deep-learning comparators. Conditional on expert-curated FA and SF masks, SFs were assigned to dorsal, ventral, and peripheral classes with 81.5%–87.3% accuracy. Controlled Poisson-Gaussian noise, Gaussian defocus, and point-spread-function blur revealed distinct, structure-specific errors in overlap and object-level measurements. By linking segmentation and reconstruction to count, area, length, apparent width, and structural class, FilAdh offers an interpretable view of the FA-actin structural system and makes degradation-dependent measurement biases explicit. BIGGER PICTURE Cells sense and respond to their physical environment through focal adhesions and actin stress fibers. The two form a mechanically coupled system, yet they are usually analyzed separately, with workflows re-tuned for each new set of images. FilAdh analyzes both within a single framework, tailoring segmentation, reconstruction, and classification to each structure’s geometry: compact adhesion patches versus thin, extended fiber networks. Testing on expert-annotated references and under controlled degradations that mimic low signal, focus errors, and optical blur shows that these common imaging imperfections distort adhesion and fiber measurements in different ways. The same design principle—matching the analysis to each structure’s shape and judging methods by how errors propagate into the final biological measurements—may benefit other bioimage-analysis problems. Released code, trained weights, and fixed data splits make all comparisons reproducible and extensible. HIGHLIGHTS FilAdh jointly quantifies focal adhesions and actin stress fibers FilAdh achieves the highest benchmarked FA overlap and comparable SF topology Matched synthetic degradations reveal geometry-specific measurement errors FA-SF spatial context enables dorsal, ventral, and peripheral SF classification
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: green
Awards: G1105844351, G1272900390, G1594323237, G1622782223, China's realities from a frontier research perspective, G410571670, G451917667, G4783475982, G488842982, G491712853, G5195188938, G5404395142, G5539308778, G5635532810, G5967599077, G624877701, G6387475415, Artificial Intelligence for Image Data Analysis in the Life Sciences, G7805626310, G7863371703, G8118709007, G8497050386, G8639778135, G90463556, G929308097
OpenAlex ID: W7218648443