Relationship Between Irregularities of the Nuclear Envelope and Mitochondria in HeLa cells Observed with Electron Microscopy

This paper describes a methodology to analyse the complexity of HeLa cells as observed with electron microscopy, in particular the relationship between mitochondria and the roughness of the nuclear envelope as reflected by the invaginations of the surface. For this purpose, several segmentation mitochondria algorithms were quantitatively compared, namely: Topology, Image Processing, Topology and Image Processing, and Deep Learning, which provided the highest accuracy. The invaginations were successfully segmented with one image processing algorithm. Metrics were extracted for both structures and correlations between the mitochondria and invaginations were explored for 25 segmented cells. It was found that there was a positive correlation between the volume of invaginations and the volume of mitochondria, and negative correlations between the number and the mean volume of mitochondria, and between the volume of the cytoplasm and the aspect ratio of mitochondria. These results suggest that there is a relationship between the shape of a cell, its nucleus and its mitochondria; as well as a relationship between the number of mitochondria and their shapes. Whilst these results were obtained from a single cell line and a relatively small number of cells, they encourage further study as the methodology proposed can be easily applied to other cells and settings

Relationship between irregularities of the nuclear envelope and mitochondria in hela cells observed with electron microscopy

This paper describes a methodology to analyse the complexity of HeLa cells as observed with electron microscopy, in particular the relationship between mitochondria and the roughness of the nuclear envelope as reflected by the invaginations of the surface. For this purpose, several mitochondria segmentation algorithms were quantitatively compared, namely: Topology, Image Processing, Topology and Image Processing, and Deep Learning, which provided the highest accuracy. The invaginations were successfully segmented with one image processing algorithm. Metrics were extracted for both structures and correlations between the mitochondria and invaginations were explored for 25 segmented cells. It was found that there was a positive correlation between the volume of invaginations and the volume of mitochondria, and negative correlations between the number and the mean volume of mitochondria, and between the volume of the cytoplasm and the aspect ratio of mitochondria. These results suggest that there is a relationship between the shape of a cell, its nucleus and its mitochondria; as well as a relationship between the number of mitochondria and their shapes. Whilst these results were obtained from a single cell line and a relatively small number of cells, they encourage further study as the methodology proposed can be easily applied to other cells and settings. Code and data are freely available. HeLa images are available from http://dx.doi.org/10.6019/EMPIAR-10094, code from https://github.com/reyesaldasoro/MitoEM, and segmented nuclei, cells, invaginations and mitochondria from https://github.com/reyesaldasoro/HeLa Cell Data

Morphological Analysis of HeLa Cells and their Mitochondria under Electron Microscopy

Changes in the shapes of the cellular nuclear envelope and mitochondria have been linked to the presence of disease and cancer. Thus, they have become an important area of study. In Computer Science, algorithms have been developed to aid in this field, by performing segmentation in order to better analyse the shapes of cells and their organelles. In this study, several segmentation algorithms are implemented to perform segmentation of HeLa cell mitochondria from images taken using an Electron Microscope. Namely, an algorithm based on Persistent Homology, another based on traditional Image Processing techniques, and a hybrid algorithm combining the previous two. The challenge posed by the Electron Microscope is extremely interesting as a high resolution image presents more complex structures which are hard to segment accurately. Additionally in this study, relationships between the morphology of cells and their mitochondria were explored and a positive correlation was found between the volume of invaginations and the volume of mitochondria, and negative correlations between the number and the mean volume of mitochondria, and between the volume of the cytoplasm and the aspect ratio of mitochondria

Image Processing and Machine Learning Techniques for Chagas Disease Detection and Identification

Chagas disease, caused by the Trypanosoma cruzi parasite, poses a significant health threat, particularly in Latin America, with millions affected globally. This research introduces a novel approach using deep learning techniques for the automated detection of Trypanosoma cruzi in blood smear images provided by Zoonoses Laboratory (CIR) in Mexico. Advanced deep learning architectures like Faster RCNN, RetinaNet, YOLOv8, and FCOS have been adapted, trained, and compared with each other in terms of the detection accuracy of each image. Our selection of those models is based on their ability to swiftly and accurately detect anomalies, measured through rigorous assessment using pivotal metrics like Mean Average Precision (mAP) across varying Intersection over Union (IoU) thresholds. Notably, the YOLOv8 model has showcased outstanding performance, boasting a remarkable mAP score of 0.951 for parasite detection and localisation. Specifically, YOLOv8 outperforms with a leading mAP of 0.951 at 50% IoU and maintains commendable precision with a score of 0.594 for IoU thresholds ranging from 50% to 95%. This research reduces dependence on skilled manual analysis holding a significant implications for healthcare in Chagas-affected regions by providing a rapid, automated solution to disease detection. This work has the potential to revolutionise diagnostics in resource-limited settings. Moreover, the models’ adaptability to other parasitic infections enhances their global health impact

Image processing and machine learning techniques for Chagas disease detection and identification

Chagas disease, caused by the Trypanosoma cruzi parasite, poses a significant health threat, particularly in Latin America, with millions affected globally. This research introduces a novel approach using deep learning techniques for the automated detection of Trypanosoma cruzi in blood smear images provided by Zoonoses Laboratory (CIR) in Mexico. Advanced deep learning architectures like Faster RCNN, RetinaNet, YOLOv8, and FCOS have been adapted, trained, and compared with each other in terms of the detection accuracy of each image. Our selection of those models is based on their ability to swiftly and accurately detect anomalies, measured through rigorous assessment using pivotal metrics like Mean Average Precision (mAP) across varying Intersection over Union (IoU) thresholds. Notably, the YOLOv8 model has showcased outstanding performance, boasting a remarkable mAP score of 0.951 for parasite detection and localisation. Specifically, YOLOv8 outperforms with a leading mAP of 0.951 at 50% IoU and maintains commendable precision with a score of 0.594 for IoU thresholds ranging from 50% to 95%. This research reduces dependence on skilled manual analysis holding a significant implications for healthcare in Chagas-affected regions by providing a rapid, automated solution to disease detection. This work has the potential to revolutionise diagnostics in resource-limited settings. Moreover, the models’ adaptability to other parasitic infections enhances their global health impact