Optimizing Synthetic Data for Enhanced Pancreatic Tumor Segmentation Article Swipe
Linkai Peng
,
Zheyuan Zhang
,
Görkem Durak
,
Frank H. Miller
,
Alpay Medetalibeyoğlu
,
Michael B. Wallace
,
Ulaş Bağcı
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.19284
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.19284
Pancreatic cancer remains one of the leading causes of cancer-related mortality worldwide. Precise segmentation of pancreatic tumors from medical images is a bottleneck for effective clinical decision-making. However, achieving a high accuracy is often limited by the small size and availability of real patient data for training deep learning models. Recent approaches have employed synthetic data generation to augment training datasets. While promising, these methods may not yet meet the performance benchmarks required for real-world clinical use. This study critically evaluates the limitations of existing generative-AI based frameworks for pancreatic tumor segmentation. We conduct a series of experiments to investigate the impact of synthetic \textit{tumor size} and \textit{boundary definition} precision on model performance. Our findings demonstrate that: (1) strategically selecting a combination of synthetic tumor sizes is crucial for optimal segmentation outcomes, and (2) generating synthetic tumors with precise boundaries significantly improves model accuracy. These insights highlight the importance of utilizing refined synthetic data augmentation for enhancing the clinical utility of segmentation models in pancreatic cancer decision making including diagnosis, prognosis, and treatment plans. Our code will be available at https://github.com/lkpengcs/SynTumorAnalyzer.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.19284
- https://arxiv.org/pdf/2407.19284
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401201585
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401201585Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2407.19284Digital Object Identifier
- Title
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Optimizing Synthetic Data for Enhanced Pancreatic Tumor SegmentationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-27Full publication date if available
- Authors
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Linkai Peng, Zheyuan Zhang, Görkem Durak, Frank H. Miller, Alpay Medetalibeyoğlu, Michael B. Wallace, Ulaş BağcıList of authors in order
- Landing page
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https://arxiv.org/abs/2407.19284Publisher landing page
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https://arxiv.org/pdf/2407.19284Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2407.19284Direct OA link when available
- Concepts
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Segmentation, Computer science, Synthetic data, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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