FAOT-Net : A 1.5-Stage Framework for 3D Pelvic Lymph Node Detection With Online Candidate Tuning
Accurate and automatic detection of pelvic lymph nodes in computed tomography (CT) scans is critical for diagnosing lymph node metastasis in colorectal cancer, which in turn plays a crucial role in its staging, treatment planning, surgical guidance, and postoperative follow-up of colorectal cancer. However, achieving high detection sensitivity and specificity poses a challenge due to the small and variable sizes of these nodes, as well as the presence of numerous similar signals within the complex pelvic CT image. To tackle these issues, we propose a 3D feature-aware online-tuning network (FAOT-Net) that introduces a novel 1.5-stage structure to seamlessly integrate detection and refinement via our online candidate tuning process and takes advantage of multi-level information through the tailored feature flow. Furthermore, we redesign the anchor fitting and anchor matching strategies to further improve detection performance in a nearly hyperparameter-free manner. Our framework achieves the FROC score of 52.8 and the sensitivity of 91.7% with 16 false positives per scan on the PLNDataset. Code will be available at: github.com/SCUsomebody/FAOT-Net/.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:43 |
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| Enthalten in: |
IEEE transactions on medical imaging - 43(2024), 3 vom: 01. März, Seite 1180-1190 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Yi [VerfasserIn] |
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| Anmerkungen: |
Date Completed 06.03.2024 Date Revised 06.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1109/TMI.2023.3329464 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Accurate and automatic detection of pelvic lymph nodes in computed tomography (CT) scans is critical for diagnosing lymph node metastasis in colorectal cancer, which in turn plays a crucial role in its staging, treatment planning, surgical guidance, and postoperative follow-up of colorectal cancer. However, achieving high detection sensitivity and specificity poses a challenge due to the small and variable sizes of these nodes, as well as the presence of numerous similar signals within the complex pelvic CT image. To tackle these issues, we propose a 3D feature-aware online-tuning network (FAOT-Net) that introduces a novel 1.5-stage structure to seamlessly integrate detection and refinement via our online candidate tuning process and takes advantage of multi-level information through the tailored feature flow. Furthermore, we redesign the anchor fitting and anchor matching strategies to further improve detection performance in a nearly hyperparameter-free manner. Our framework achieves the FROC score of 52.8 and the sensitivity of 91.7% with 16 false positives per scan on the PLNDataset. Code will be available at: github.com/SCUsomebody/FAOT-Net/ | ||
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| 700 | 1 | |a Li, Xinyang |e verfasserin |4 aut | |
| 700 | 1 | |a Xie, Min |e verfasserin |4 aut | |
| 700 | 1 | |a Islam, Md Tauhidul |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Haixian |e verfasserin |4 aut | |
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