Weighted average ensemble-based semantic segmentation in biological electron microscopy images.
Semantic segmentation of electron microscopy images using deep learning methods is a valuable tool for the detailed analysis of organelles and cell structures. However, these methods require a large amount of labeled ground truth data that is often unavailable. To address this limitation, we present...
| Publicado en: | Histochemistry & Cell Biology Vol. 158; no. 5; pp. 447 - 463 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160028870&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160028870 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09486143 NQI jtl: Histochemistry & Cell Biology issn: 09486143 maglogo: N pubinfo: dt: Nov2022 vid: 158 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 160028870 158602094 10.1007/s00418-022-02148-3 160028870 ppf: 447 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Weighted average ensemble-based semantic segmentation in biological electron microscopy images. aug: au: Shaga Devan, Kavitha Kestler, Hans A. Read, Clarissa Walther, Paul affil: Central Facility of Electron Microscopy, Ulm University, Albert Einstein-Allee 11, 89081, Ulm, Germany sug: ab: Semantic segmentation of electron microscopy images using deep learning methods is a valuable tool for the detailed analysis of organelles and cell structures. However, these methods require a large amount of labeled ground truth data that is often unavailable. To address this limitation, we present a weighted average ensemble model that can automatically segment biological structures in electron microscopy images when trained with only a small dataset. Thus, we exploit the fact that a combination of diverse base-learners is able to outperform one single segmentation model. Our experiments with seven different biological electron microscopy datasets demonstrate quantitative and qualitative improvements. We show that the Grad-CAM method can be used to interpret and verify the prediction of our model. Compared with a standard U-Net, the performance of our method is superior for all tested datasets. Furthermore, our model leverages a limited number of labeled training data to segment the electron microscopy images and therefore has a high potential for automated biological applications. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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