Toxoplasma gondii is a parasite that chronically infects about a third of the world's population. During chronic infection, the parasite resides within tissue cysts in the form of poorly understood bradyzoites which can number in the thousands. Our prior work showed that these bradyzoites are metabolically active exhibiting heterogeneous replication potential. The morphological plasticity of the mitochondrion potentially informs about parasite metabolic state. We developed an image processing based program to assist manual classification of mitochondrial morphologies by trained operators to collect data and statistics from the manual classification of shapes. We sought to determine whether certain morphologies were readily classifiable and the congruence among manual classifiers, i.e. the degree to which different operators would place the same objects within the same class. Results from three operators classifying mitochondrial morphologies from 5 tissue cyst images showed that among the four classes, one (Blobs) were the easiest to classify. There was remarkable congruence between 2 of the 3 operators in classifying the objects (96%), while the agreement among all 3 operators was somewhat modest (57%). Such information would be valuable for biologists studying these parasites as well as in development of fully automated methods of morphological classification.
|Title of host publication||43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021|
|Number of pages||5|
|State||Published - 2021|
|Event||43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 - Virtual, Online, Mexico|
Duration: Nov 1 2021 → Nov 5 2021
|Name||Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS|
|Conference||43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021|
|Period||11/1/21 → 11/5/21|
Bibliographical noteFunding Information:
Research supported by National Institute of Health grant R01AI145335 awarded to APS and AP (MPI's).
© 2021 IEEE.
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics