Position Prior Attention Network for Pancreas Tumor Segmentation
Segmentation of pancreatic tumors on CT images is essential for the diagnosis and treatment of pancreatic cancer. However, low contrast between the pancreas and the tumor, as well as variable tumor shape and position, makes segmentation challenging. To solve the problem, we propose a Position Prior Attention Network (PPANet) with a pseudo segmentation generation module (PSGM) and a position prior attention module (PPAM). PSGM and PPAM maps pancreatic and tumor pseudo segmentation to latent space to generate position prior attention map and supervises location classification. The proposed method is evaluated on pancreatic patient data collected from local hospital and the experimental results demonstrate that our method can significantly improve the tumor segmentation results by introducing the position information in the training phase.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:310 |
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| Enthalten in: |
Studies in health technology and informatics - 310(2024) vom: 25. Jan., Seite 951-955 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Dong, Kaiqi [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 26.01.2024 Date Revised 26.01.2024 published: Print Citation Status MEDLINE |
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| doi: |
10.3233/SHTI231105 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM367603756 |
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| 520 | |a Segmentation of pancreatic tumors on CT images is essential for the diagnosis and treatment of pancreatic cancer. However, low contrast between the pancreas and the tumor, as well as variable tumor shape and position, makes segmentation challenging. To solve the problem, we propose a Position Prior Attention Network (PPANet) with a pseudo segmentation generation module (PSGM) and a position prior attention module (PPAM). PSGM and PPAM maps pancreatic and tumor pseudo segmentation to latent space to generate position prior attention map and supervises location classification. The proposed method is evaluated on pancreatic patient data collected from local hospital and the experimental results demonstrate that our method can significantly improve the tumor segmentation results by introducing the position information in the training phase | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Pancreas tumor segmentation | |
| 650 | 4 | |a position prior knowledge | |
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| 700 | 1 | |a Hu, Peijun |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Xiang |e verfasserin |4 aut | |
| 700 | 1 | |a Tian, Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Yan |e verfasserin |4 aut | |
| 700 | 1 | |a Bai, Xueli |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Tingbo |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jingsong |e verfasserin |4 aut | |
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