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Hokkaido University Collection of Scholarly and Academic Papers >
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Deep Learning-Based Nuclear Lobe Count Method for Differential Count of Neutrophils
| Title: | Deep Learning-Based Nuclear Lobe Count Method for Differential Count of Neutrophils |
| Authors: | Yabuta, Mayu Browse this author | | Nakamura, Iori Browse this author | | Ida, Haruhi Browse this author | | Masauzi, Hiromi Browse this author | | Okada, Kazunori Browse this author | | Kaga, Sanae Browse this author →KAKEN DB | | Miwa, Keiko Browse this author →KAKEN DB | | Masauzi, Nobuo Browse this author |
| Keywords: | blood cell automatic image analysis | | computer vision | | convolutional neural networks | | deep learning | | white blood cell morphology |
| Issue Date: | Jul-2021 |
| Publisher: | Tohoku University Medical Press |
| Journal Title: | The Tohoku journal of experimental medicine |
| Volume: | 254 |
| Issue: | 3 |
| Start Page: | 199 |
| End Page: | 206 |
| Publisher DOI: | 10.1620/tjem.254.199 |
| Abstract: | Differentiating neutrophils based on the count of nuclear lobulation is useful for diagnosing various hematological disorders, including megaloblastic anemia, myelodysplastic syndrome, and sepsis. It has been reported that one-fifth of sepsis-infected patients worldwide died between 1990 and 2017. Notably, fewer nuclear-lobed and stab-formed neutrophils develop in the peripheral blood during sepsis. This abnormality can serve as an early diagnostic criterion. However, testing this feature is a complex and time-consuming task that is rife with human error. For this reason, we apply deep learning to automatically differentiate neutrophil and nuclear lobulation counts and report the world's first small-scale pilot. Blood films are prepared using venous peripheral blood taken from four healthy volunteers and are stained with May Grunwald Giemsa stain. Six-hundred 360 x 363-pixel images of neutrophils having five different nuclear lobulations are automatically captured by Cellavision DM-96, an automatic digital microscope camera. Images are input to an original architecture with five convolutional layers built on a deep learning neural-network platform by Sony, Neural Network Console. The deep learning system distinguishes the four groups (i.e., band-formed, two-, three-, and four- and five-segmented) of neutrophils with up to 99% accuracy, suggesting that neutrophils can be automatically differentiated based on their count of segmented nuclei using deep learning. |
| Type: | article |
| URI: | http://hdl.handle.net/2115/82923 |
| Appears in Collections: | 保健科学院・保健科学研究院 (Graduate School of Health Sciences / Faculty of Health Sciences) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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