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Optimizing Synthetic Data for Enhanced Pancreatic Tumor Segmentation

Linkai Peng, Zheyuan Zhang, Gorkem Durak, Frank H. Miller, Alpay Medetalibeyoglu, Michael B. Wallace, Ulas Bagci

TL;DR

These 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.

Abstract

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.

Optimizing Synthetic Data for Enhanced Pancreatic Tumor Segmentation

TL;DR

These 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.

Abstract

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.
Paper Structure (11 sections, 2 equations, 2 figures, 3 tables)

This paper contains 11 sections, 2 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Tumor Generation Network
  4. Dataset
  5. Impact of tumor size
  6. Impact of boundary
  7. Experiments and Results
  8. Training protocol
  9. Influence of tumor size
  10. Influence of noisy label
  11. Discussion and Conclusion

Figures (2)

  • Figure 1: Schematic demonstration of our proposed verification strategy. Panel $\textbf{(a)}$ shows the tumor segmentation process using a diffusion model to synthesize pancreatic tumors. $\boldsymbol{E}$ and $\boldsymbol{D}$ denote the encoder and decoder of a pre-trained autoencoder. Panel $\textbf{(b)}$ depicts two proposed verification methods. The upper part shows the generation of fixed-size tumors for segmentation. The lower part illustrates the elastic deformation used for generating noisy labels.
  • Figure 2: Qualitative visualization results of all compared methods. The rows represent the models used, while the columns display results from left to right: raw input volumes, labels, results without synthetic volumes, and segmentation for tumors categorized as Tiny, Small, Medium, Large, Mixed, and with Noisy labels. Tumor boundaries are delineated in the figure.