Segment 2D and 3D Filaments by Learning Structured and Contextual Features
We focus on the challenging problem of filamentary structure segmentation in both 2D and 3D images, including retinal vessels and neurons, among others. Despite the increasing amount of efforts in learning based methods to tackle this problem, there still lack proper data-driven feature construction mechanisms to sufficiently encode contextual labelling information, which might hinder the segmentation performance. This observation prompts us to propose a data-driven approach to learn structured and contextual features in this paper. The structured features aim to integrate local spatial label patterns into the feature space, thus endowing the follow-up tree classifiers capability to grouping training examples with similar structure into the same leaf node when splitting the feature space, and further yielding contextual features to capture more of the global contextual information. Empirical evaluations demonstrate that our approach outperforms state-of-the-arts on well-regarded testbeds over a variety of applications. Our code is also made publicly available in support of the open-source research activities..
| Medienart: |
Artikel |
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| Erscheinungsjahr: |
2017 |
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| Erschienen: |
2017 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:36 |
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| Enthalten in: |
IEEE transactions on medical imaging - 36(2017), 2, Seite 596-606 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Gu, Lin [VerfasserIn] |
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| RVK: |
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| doi: |
10.1109/TMI.2016.2623357 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a We focus on the challenging problem of filamentary structure segmentation in both 2D and 3D images, including retinal vessels and neurons, among others. Despite the increasing amount of efforts in learning based methods to tackle this problem, there still lack proper data-driven feature construction mechanisms to sufficiently encode contextual labelling information, which might hinder the segmentation performance. This observation prompts us to propose a data-driven approach to learn structured and contextual features in this paper. The structured features aim to integrate local spatial label patterns into the feature space, thus endowing the follow-up tree classifiers capability to grouping training examples with similar structure into the same leaf node when splitting the feature space, and further yielding contextual features to capture more of the global contextual information. Empirical evaluations demonstrate that our approach outperforms state-of-the-arts on well-regarded testbeds over a variety of applications. Our code is also made publicly available in support of the open-source research activities. | ||
| 650 | 4 | |a neuronal reconstruction | |
| 650 | 4 | |a Three-dimensional displays | |
| 650 | 4 | |a random forests | |
| 650 | 4 | |a 2D & 3D neuronal segmentation | |
| 650 | 4 | |a Context | |
| 650 | 4 | |a Retinal vessel segmentation | |
| 650 | 4 | |a Two dimensional displays | |
| 650 | 4 | |a Training | |
| 650 | 4 | |a Image segmentation | |
| 650 | 4 | |a feature learning | |
| 650 | 4 | |a Feature extraction | |
| 650 | 4 | |a Boosting | |
| 700 | 1 | |a Zhang, Xiaowei |4 oth | |
| 700 | 1 | |a Zhao, He |4 oth | |
| 700 | 1 | |a Li, Huiqi |4 oth | |
| 700 | 1 | |a Cheng, Li |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |t IEEE transactions on medical imaging |d New York, NY [u.a.] : IEEE, 1982 |g 36(2017), 2, Seite 596-606 |w (DE-627)130411280 |w (DE-600)622531-7 |w (DE-576)015914445 |x 0278-0062 |7 nnns |
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| 856 | 4 | 2 | |u http://ieeexplore.ieee.org/document/7725981 |
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