Artificial intelligence can revolutionize scientific projects, but scientists face challenges in reusing, integrating, and deploying cost-effective and high-quality machine learning solutions. Determining suitable algorithms and parameters is difficult, especially for non-programmer scientists. Some algorithms, like deep learning-based methods, offer flexibility but require extensive training on annotated data. This poses a hurdle in labor-intensive tasks like biological image segmentation that relies on expert annotations. In this paper, we present a data-driven framework designed to assist scientists in selecting, reusing, and training machine learning solutions for microscopy image segmentation. The framework is based on establishing a mapping between object morphology features and the optimal segmentation algorithms and settings for individual objects. This mapping is iteratively refined through a combination of unsupervised learning and active learning iterations. To expedite convergence, objects are initially clustered based on their morphology. In each active learning iteration, the most informative and uncertain samples are selected and queried within a specific cluster. Through a biological case study, we demonstrate that our method enables the selection and training of segmentation algorithms specific to object types. Additionally, the selective requests for user input significantly reduce the number of user interactions required for this task.

Machine learning techniques have revolutionised scientific software projects. Scientists are continuously looking for novel approaches to production-quality reuse of machine learning solutions and to make them available to other components of the project with satisfactory quality and low costs. However, scientists often have limited knowledge about how to effectively reuse and adjust machine learning solutions in their particular scientific project. One challenge is that many machine learning solutions require parameter tuning based on the input data to achieve satisfactory results, which is difficult and cumbersome for users not familiar with machine learning. Autotuning is the common technique for potentially adjusting the parameters based on the data, but it requires a well-defined objective function to optimize for. Such an objective function is commonly unknown in exploratory scientific research such as biological image segmentation tasks. In this paper, we propose a framework based on the novel combination of autotuning and active learning to ease and partially automate the reuse effort of machine learning solutions for scientists in biological image segmentation cases. Underlying this combination is a mapping between an object type and specific parameters applied during the segmentation process. This mapping is iteratively adjusted by asking users for visual feedback. We then through a biological case study demonstrate that our method enables tuning of the segmentation specifically to object types, while the selective requests of user input reduce the number of user interactions required for this task.