Bao, Shunxing; Xiao, Jiteng; Wang, Qiushui; Liu, Wenjing; Ren, Juan; Chen, Ting; An, Yunhe. (2026).听.听Sensors, 26(18), 5971.听
Brain organoids are laboratory-grown models of the brain, and reliably assessing their quality is important for research. Currently, this assessment often relies on researchers visually inspecting microscope images. We investigated whether measurable image features could help automate this process. Using 692 images from six culture batches, we compared several image-analysis approaches for predicting quality ratings assigned by an expert. A model based on interpretable, handcrafted image features performed well overall, although accuracy varied considerably between culture batches. These findings provide an initial foundation for automated quality assessment of brain organoids using standard microscope images. However, further studies are needed to confirm the consistency of expert quality ratings and determine how well the approach performs across different laboratories and culture conditions.
Figure 1.聽Motivation and study framework for interpretable brightfield organoid quality assessment. (A) Routine manual QA is based on qualitative observations of compactness, boundary clarity, and internal appearance. (B) Organoid appearance changes across developmental stages and culture batches, and images obtained on different culture days represent independent sampling events rather than longitudinal tracking of the same individual organoid. (C) The study asks whether these visual observations can be translated into interpretable optical descriptors across developmental stages and independently within maturation. (D) Handcrafted feature families are compared with frozen ImageNet-pretrained ResNet-50 and self-supervised DINOv2 representations using logistic-regression and random-forest classifiers. (E) Evaluation includes repeated QA-stratified cross-validation, leakage-safe preprocessing, maturation-only analysis, and leave-one-batch-out testing.