BactSegNet: A Growth-Stage-Aware, Searched Instance-Segmentation Model for Salmonella and Escherichia coli Microcolonies in Pure-Culture Bright-Field Microscopy with a Food-Matrix Surrogate

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Instance segmentation of small, dense, low-contrast microcolonies against confounding food matrices limits early microscopy-based screening for Salmonella and E. coli. We present BactSegNet, combining EfficientNetV2, BiFPN, selective-kernel attention, and growth-stage FiLM conditioning based on a learnable embedding of recorded acquisition stage. BE-NAS explored a 2304-configuration search space and evaluated 71 unique architectures. Across twelve retrained candidates, proxy and full-training rankings agreed (Spearman ρ=0.867 for mAP@50 and ρ=0.790 for mAP@50–95). Across the 71 architectures evaluated in the BE-NAS proxy search, three-seed proxy fitness showed high reliability (ICC(1,1)=0.91; 213 seed-level evaluations), while phenotype caching reduced 180 scheduled evaluations to 71 unique trainings (213 rather than 540 seed-level proxy runs). On 14,318 expert-verified polygon instances spanning six stages, two species, and onion-present/absent conditions, BactSegNet-L achieved mask mAP@50 = 0.9636 and mAP@50–95 = 0.7154, 3.56 percentage points above a multi-seed YOLOv8 baseline and higher mAP@50 than sixteen class-aware single-run references across three paradigms. A capacity-preserving stage-perturbation control reduced mAP@50 from 0.9636±0.0049 with correct stage assignments to 0.9487±0.0040 after shuffling image–stage correspondence across three seeds. Gains reached 8.66 pp for Salmonella at 1.5 h and 4.90 pp on onion. Across 2756 matched instances, area MAPE was 12.15% with r=0.997, dominated by the largest instances. BactSegNet-Efficient retained 0.9510 mAP@50 with 19.1% fewer FLOPs at 43.9 fps. The main dataset comprises pure-culture frames; an independent, expert-annotated mixed-culture evaluation (400 images, 3.5 and 4.0 h) yielded mask mAP@50=0.8857±0.0206 across three seeds without retraining. Recorded growth-stage metadata are required at inference, unlike the image-only comparison models. These results support early, physically calibrated microcolony segmentation while distinguishing the original pure-culture evaluation from the additional mixed-culture test.