Comparing Image Segmentation Neural Networks for the Analysis of Precision Cut Lung Slices

Bronchodilators serve as a pivotal intervention for ameliorating symptoms associated with Inflammatory and allergic lung diseases. The objective assessment of bronchodilator efficacy is critical for therapeutic optimization. Measuring airflowvolume through precision cut lung slices (PCLS) imaging at varying time intervals provides a quantitative means to assess airway patency. To enhance the efficiency of this evaluation process, our study extends the existing image segmentation workflow to encompass a wider range of neural networks. Extensive experiments have been conducted across varied data preprocessing methods and loss functions. Furthermore, we contrast the performance differences between single and ensemble models, alongside a visual comparative analysis of their detailed variances in image segmentation. This refined workflow not only surpasses previous experimental results but also enhances the accuracy of lung treatment programs, offering a broader array of choices for future image segmentation tasks.