Name
Title | ||
|---|---|---|
Characterizing robustness and sensitivity of convolutional neural networks for quantitative analysis of mitochondrial morphology |
Abstract | ||
|---|---|---|
Quantitative analysis of mitochondrial morphology plays important roles in studies of mitochondrial biology. The analysis depends critically on segmentation of mitochondria, the image analysis process of extracting mitochondrial morphology from images. The main goal of this study is to characterize the performance of convolutional neural networks (CNNs) in segmentation of mitochondria from fluorescence microscopy images. Recently, CNNs have achieved remarkable success in challenging image segmentation tasks in several disciplines. So far, however, our knowledge of their performance in segmenting biological images remains limited. In particular, we know little about their robustness, which defines their capability of segmenting biological images of different conditions, and their sensitivity, which defines their capability of detecting subtle morphological changes of biological objects. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s40484-018-0156-3 | Quantitative Biology |
Keywords | DocType | Volume |
convolutional neural network,mitochondrial morphology,image segmentation,robustness,sensitivity | Journal | 6 |
Issue | ISSN | Citations |
4 | 2095-4697 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoqi Chai | 1 | 5 | 1.57 |
| Qinle Ba | 2 | 0 | 1.35 |
| Ge Yang | 3 | 1 | 0.74 |