Abstract Online user feedback, like app reviews, can provide valuable insights into software product improvements, offering development teams direct insights into customer experiences, preferences, and pain points. There are many studies that have proposed promising methods to automatically prioritize online user feedback, helping development teams identify the most salient software issues that need to be addressed. However, these methods may not take into account the accessibility-related needs of end users. Our study addresses this limitation by developing a novel approach to analyze and prioritize app store reviews that discuss accessibility concerns. This new approach involves the evaluation of seven distinct machine learning (ML) algorithms, as well as three state-of-the-art large language models (LLMs), all leveraging features of app reviews relevant to accessibility. Utilizing validated accessibility reviews, we assess the effectiveness of our proposed approach and compare its performance with a leading general prioritization tool. The results show that our novel method surpasses the leading general tool in prioritizing accessibility reviews, achieving an F1-score of 83.6%. This represents an improvement over the prior study’s F1-score of 69.0%. Additionally, our approach outperforms the existing method across all three priority classifications, with the most notable improvement seen in the identification of high-priority reviews, where we achieved a + 59.8% increase in F1-score. We hope our findings will inspire more research and innovation in this area and ultimately contribute to a more inclusive and accessible digital landscape for all users.

Synthetic images are widely used in image segmentation for algorithm training and performance assessment. Recently, advances in image synthesis techniques, especially generative adversarial networks (GANs), have made it possible to generate fluorescence microscopy images with remarkably realistic appearance. However, intuitive and specific metrics to assess the quality of these images remain lacking. Here, we propose three quality metrics that quantify the fidelity of the foreground signal, the background noise, and blurring, respectively, of synthesized fluorescence microscopy images. Using these metrics, we examine images of mitochondria synthesized by two representative GANs: pix2pix, which requires paired training data, and CycleGAN, which does not require paired training data. We find that both networks generate realistic images and achieve similar fidelity in reproducing background noise and blurring of real images. However, CycleGAN achieves significantly higher fidelity than pix2pix in reproducing intensity patterns of real mitochondria. When used to train the U-Net for segmentation, images synthesized by both networks achieve performance on par with real images. Overall, we have developed a method to assess quality of synthetic fluorescence microscopy images and to evaluate their training performance in image segmentation. The quality metrics proposed are general and can be used to assess fluorescence microscopy images synthesized by different methods.

Convolutional neural networks (CNNs) recently have achieved remarkable success in segmentation of biological fluorescence microscopy images. Because many of these networks were developed initially for general computer vison tasks such as object detection and object recognition, it is necessary to characterize their performance to determine how they meet the needs of related biological studies. So far, performance characterization of such networks has focused primarily on segmentation accuracy. It remains unclear how different networks compare in their robustness in handling images of different conditions and their sensitivity in detecting subtle geometrical changes of biological structures. Here, we develop a method that uses realistic synthetic images to characterize the robustness and sensitivity of such networks. We use the method to compare the performance of two widely adopted CNNs: the fully convolutional network (FCN) and the U-Net, in segmentation of complex morphology of mitochondria. We also compare them against an adaptive active-mask algorithm in performance. We find that both networks outperform the adaptive active-mask algorithm in robustness and sensitivity and that U-Net outperforms FCN Overall, our study provides new insights into the performance of CNNs in segmentation of fluorescence microscopy images.

Neurons depend critically on active transport of cargoes throughout their complex neurite networks for their survival and function. Defects in this process have been strongly associated with many human neurodevelopmental and neurodegenerative diseases. To understand related neuronal physiology and disease mechanisms, it is essential to measure the traffic flow within the neurite networks. Currently, however, image analysis methods required for this measurement are lacking. To address this deficiency, we developed a method that could measure the flow rates of cargo traffic at any specified locations along individual branches of the neurite networks. Our method is based on detecting and counting cargo trajectories passing through the specified locations of measurement in kymographs, which are spatiotemporal maps of cargo movement within one-dimensional neurites. A main focus of our method development is robust performance, which ensures that our method works reliably and accurately under low signal-to-noise ratios. We validated and benchmarked our method using both synthetic and actual image data and found its accuracy to be >85% on average under normal conditions. Our method can be used to measure traffic flow in not just neurite networks but also other intracellular networks such as cytoskeletal filament networks.

MOTIVATION: Cellular Electron CryoTomography (CECT) enables 3D visualization of cellular organization at near-native state and in sub-molecular resolution, making it a powerful tool for analyzing structures of macromolecular complexes and their spatial organizations inside single cells. However, high degree of structural complexity together with practical imaging limitations makes the systematic de novo discovery of structures within cells challenging. It would likely require averaging and classifying millions of subtomograms potentially containing hundreds of highly heterogeneous structural classes. Although it is no longer difficult to acquire CECT data containing such amount of subtomograms due to advances in data acquisition automation, existing computational approaches have very limited scalability or discrimination ability, making them incapable of processing such amount of data. RESULTS: To complement existing approaches, in this article we propose a new approach for subdividing subtomograms into smaller but relatively homogeneous subsets. The structures in these subsets can then be separately recovered using existing computation intensive methods. Our approach is based on supervised structural feature extraction using deep learning, in combination with unsupervised clustering and reference-free classification. Our experiments show that, compared with existing unsupervised rotation invariant feature and pose-normalization based approaches, our new approach achieves significant improvements in both discrimination ability and scalability. More importantly, our new approach is able to discover new structural classes and recover structures that do not exist in training data. AVAILABILITY AND IMPLEMENTATION: Source code freely available at http://www.cs.cmu.edu/∼mxu1/software . CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.