Benchmarking the Cell Image Segmentation Models Robustness under the Microscope Optical Aberrations

Cell segmentation is essential in biomedical research for analyzing cellular morphology and behavior. Deep learning methods, particularly convolutional neural networks (CNNs), have revolutionized cell segmentation by extracting intricate features from images. However, the robustness of these methods under microscope optical aberrations remains a critical challenge. This study comprehensively evaluates the performance of cell instance segmentation models under simulated aberration conditions using the DynamicNuclearNet (DNN) and LIVECell datasets. Aberrations, including Astigmatism, Coma, Spherical, and Trefoil, were simulated using Zernike polynomial equations. Various segmentation models, such as Mask R-CNN with different network heads (FPN, C3) and backbones (ResNet, VGG19, SwinS), were trained and tested under aberrated conditions. Results indicate that FPN combined with SwinS demonstrates superior robustness in handling simple cell images affected by minor aberrations. Conversely, Cellpose2.0 proves effective for complex cell images under similar conditions. Our findings provide insights into selecting appropriate segmentation models based on cell morphology and aberration severity, enhancing the reliability of cell segmentation in biomedical applications. Further research is warranted to validate these methods with diverse aberration types and emerging segmentation models. Overall, this research aims to guide researchers in effectively utilizing cell segmentation models in the presence of minor optical aberrations.

FairytaleCQA: Integrating a Commonsense Knowledge Graph into Children's Storybook Narratives

AI models (including LLM) often rely on narrative question-answering (QA) datasets to provide customized QA functionalities to support downstream children education applications; however, existing datasets only include QA pairs that are grounded within the given storybook content, but children can learn more when teachers refer the storybook content to real-world knowledge (e.g., commonsense knowledge). We introduce the FairytaleCQA dataset, which is annotated by children education experts, to supplement 278 storybook narratives with educationally appropriate commonsense knowledge. The dataset has 5,868 QA pairs that not only originate from the storybook narrative but also contain the commonsense knowledge grounded by an external knowledge graph (i.e., ConceptNet). A follow-up experiment shows that a smaller model (T5-large) fine-tuned with FairytaleCQA reliably outperforms much larger prompt-engineered LLM (e.g., GPT-4) in this new QA-pair generation task (QAG). This result suggests that: 1) our dataset brings novel challenges to existing LLMs, and 2) human experts' data annotation are still critical as they have much nuanced knowledge that LLMs do not know in the children educational domain.