# Image Processing and Analysis for Biotechnology and Bioprocess Engineering
## 2 An Overview of Published Articles
Understanding their optical properties is crucial for the successful utilization of these substances. In nature, C-PC exists in complex formations with various substances, and the aggregation of these complexes can impact their optical properties. Therefore, the authors explored the optical properties of C-PC aggregates. While most previous studies focused on the absorbance spectrum, the authors aimed to interpret fluorescence decay using mathematical models. For this purpose, C-PC aggregate was separated from _S. maxima_ by precipitation and ion exchange, and then was followed by gel chromatography. The gel electrophoresis image confirmed the molecular weight of C-PC aggregates to be between approximately 17 and 20 kDa. The authors employed the Varioskan LUX multimode microplate reader (Thermo Scientific, Waltham, MA, USA) to study fluorescence decay and continuously imaged the fluorescence intensity emitted during excitation at 609 nm for 100 s. According to the analysis results, the C-PC aggregates exhibited a non-linear decay in fluorescence intensity over time. To mathematically simulate this decay trend, the authors devised two differential equation models based on mass balance. These mathematical models successfully regressed the experimental results of fluorescence intensity decay over time for standard C-PC and three C-PC aggregates. The article by [PERSON] et al. (Contribution 4) presents the development of deep neural network (DNN)-based segmentation algorithms for the automatic annotation of blob objects in images. Modern machine learning-based object detection and image classification usually require the preparation of training data and manual work by humans to label (or annotate) objects present in the image. When there are a large number of images or objects to be annotated, performing this task manually can be time-consuming, prone to inconsistencies, and susceptible to human errors. Especially in images where there is little difference in brightness and contrast between objects and the background, applying classical image processing algorithms based on pixel-by-pixel processing, such as adaptive thresholding or watershed, can be challenging. The images considered in this paper were captured using plasmon-assisted microscopy, utilizing the surface plasmon resonance effect (SPR), to image viruses and virus-like particles. These images fall into a category where accurate segmentation is difficult using classical segmentation algorithms. The authors of this article developed a technology using DNN for the automatic segmentation of such images. An interesting aspect is that, during training, instead of relying on individual annotation information for valid objects present in the images, they depended on the count of objects within the images. To achieve this, they connected a counting head, specialized in object counting, to the existing DNN, making the counting head responsible for object counting during training. In other words, the counting head was integrated into the network, and training was conducted using counting loss to ensure that the number of objects in the segmented result matches the manually provided count. However, relying solely on counting losses for training does not guarantee that the segmented images will necessarily exhibit a valid blob-like object. To address this, two approaches were devised. The first method involves utilizing class activation mapping (CAM) along with counting losses. The second method, instead of using CAM, adds contrast loss and morphological loss to counting loss, defining a total loss for training and segmentation. The authors conducted segmentation training using these two methods, using synthetically generated SPR images along with real SPR images. Both methods exhibited high segmentation accuracy, as measured by precision, recall, and F1-score, demonstrating the effective functioning of these weakly supervised automatic segmentation techniques.