Automated Cardiac Chamber Size and Cardiac Physiology Measurement in Water Fleas by U-Net and Mask RCNN Convolutional Networks

SIMPLE SUMMARY: With the rapid development of technology, artificial intelligent become a major breakthrough that can help human with laborious job. Previously cardiac imaging in Daphnia was also suffer from laborious and tedious process to extract some information from it. Thus the aim of this study was to develop a simple artificial intelligent based method to help anyone in this field to perform analysis in fast, reliable, and less tedious manner. In this study, we compare U-Net and Mask RCNN and found out that Mask RCNN was perform better than U-Net in cardiac chamber area estimation. From this data, several parameter like heart rhythm, stroke volume, ejection fraction, fractional shortening, and cardiac output can be extracted. The validation was done by comparing the normal and Roundup exposed group and it show that Roundup can increase the stroke volume, cardiac output, and the shortening fraction of Daphnia magna. ABSTRACT: Water fleas are an important lower invertebrate model that are usually used for ecotoxicity studies. Contrary to mammals, the heart of a water flea has a single chamber, which is relatively big in size and with fast-beating properties. Previous cardiac chamber volume measurement methods are primarily based on ImageJ manual counting at systolic and diastolic phases which suffer from low efficiency, high variation, and tedious operation. This study provides an automated and robust pipeline for cardiac chamber size estimation by a deep learning approach. Image segmentation analysis was performed using U-Net and Mask RCNN convolutional networks on several different species of water fleas such as Moina sp., Daphnia magna, and Daphnia pulex. The results show that Mask RCNN performs better than U-Net at the segmentation of water fleas’ heart chamber in every parameter tested. The predictive model generated by Mask RCNN was further analyzed with the Cv2.fitEllipse function in OpenCV to perform a cardiac physiology assessment of Daphnia magna after challenging with the herbicide of Roundup. Significant increase in normalized stroke volume, cardiac output, and the shortening fraction was observed after Roundup exposure which suggests the possibility of heart chamber alteration after roundup exposure. Overall, the predictive Mask RCNN model established in this study provides a convenient and robust approach for cardiac chamber size and cardiac physiology measurement in water fleas for the first time. This innovative tool can offer many benefits to other research using water fleas for ecotoxicity studies.