SpheroSeg: Advancing tumor spheroid analysis through open-source deep learning

Three-dimensional tumor spheroids are widely used in vitro models for studying tumor growth, invasion, and treatment response. Quantitative analysis remains challenging because imaging conditions vary across experiments, annotation policies differ between datasets, and large expert-corrected segmentation datasets are limited. This study introduces SpheroSeg, an open-source platform for bright-field spheroid segmentation that combines a large annotated dataset, standardized model benchmarking, and an accessible web-based analysis workflow.

We introduced SpheroHQ, a dataset comprising 22,683 bright-field images with expert-corrected annotations across seven cancer cell lines, and SpheroMix, a 32,367-image corpus integrating SpheroHQ with external spheroid datasets. Eight deep-learning segmentation architectures, including convolutional, attention-based, and state-space designs, were evaluated under a unified two-stage protocol and assessed on three stratified test sets: within-distribution SpheroHQ images, externally annotated DTS images, and the held-out out-of-distribution HTS-Seg dataset. Performance was assessed using standard segmentation metrics, with bootstrap 95% confidence intervals reported in the full benchmark to separate within-dataset performance, annotation-policy effects, and out-of-distribution generalization. A complementary SegFormer analysis was performed under the same stratified evaluation protocol to assess transformer-based segmentation models.

Within the main eight-architecture benchmark, the best-performing models differed across test settings. CBAM-ResUNet achieved the highest within-distribution SpheroHQ IoU (0.9478), whereas MambaBot-UNet achieved the highest externally annotated DTS IoU (0.9351) and the strongest held-out HTS-Seg IoU (0.5867) within the main benchmark. The stratified analysis revealed a substantial gap between within-distribution and held-out out-of-distribution performance, indicating that the released models should be interpreted within the documented imaging and annotation conditions. In the complementary transformer analysis, SegFormer-B0 provided a fast transformer option with strong externally annotated DTS performance (IoU = 0.9449) and competitive HTS-Seg performance (IoU = 0.5139). The deployed web application provides three complementary model options: CBAM-ResUNet, SegFormer-B0, and MambaBot-UNet.

SpheroSeg contributes a large expert-corrected spheroid dataset, a protocol-aware benchmark, and a hosted and Dockerized web application for GPU-accelerated spheroid segmentation, polygon-based correction, morphometric measurement, and export for downstream analysis. Rather than serving as a universal spheroid detector, SpheroSeg provides a reproducible reference benchmark and deployable analysis platform for the documented bright-field imaging and annotation settings.