Discovering Digital Tumor Signatures-Using Latent Code Representations to Manipulate and Classify Liver Lesions
Jens Kleesiek1,2,3,4, Benedikt Kersjes2, Kai Ueltzhöffer5
1Institute for AI in Medicine (IKIM), University Medicine Essen, 45131 Essen, Germany.
Cancers
|July 2, 2021
Summary
This study introduces "digital tumor signatures" from deep learning for liver cancer imaging. These signatures enable synthetic data generation and accurate differentiation between liver lesions and normal tissue.
Area of Science:
- Oncological Medical Imaging
- Deep Learning
- Medical Image Analysis
Background:
- Generative deep learning (DL) enables unsupervised learning of latent representations.
- These representations, termed "digital tumor signatures," can potentially aid in oncological medical imaging tasks.
- Current methods may benefit from advanced unsupervised learning for lesion characterization and data augmentation.
Purpose of the Study:
- To investigate the utility of digital tumor signatures for differentiating liver lesions from normal tissue.
- To explore the application of these signatures in generating synthetic CT images with user-defined liver tumor insertion and removal.
- To evaluate the performance of an unsupervised implicit autoencoder model for these tasks.
Main Methods:
- Utilized an implicit autoencoder, combining autoencoder and generative adversarial network (GAN)-like components.
- Trained the model on abdominal CT scans, requiring minimal data for synthetic image generation.
- Employed Principal Component Analysis (PCA) embedding for latent representation analysis and machine learning classifiers (LinearSVM) for tissue differentiation.
Main Results:
- Demonstrated successful generation of synthetic liver CT images with realistic tumor lesion insertion/removal, indistinguishable from real images by expert radiologists.
- Achieved high accuracy (up to 97%), sensitivity (up to 95%), and specificity (up to 99%) in discriminating between liver lesions and normal tissue using digital tumor signatures.
- Showcased the effectiveness of unsupervised learning with minimal data for complex medical imaging applications.
Conclusions:
- The proposed unsupervised learning paradigm effectively generates synthetic medical images and facilitates targeted manipulation of liver lesions.
- "Digital tumor signatures" derived from deep learning offer a powerful tool for differentiating cancerous lesions from healthy tissue in CT scans.
- This approach holds significant potential for advancing oncological imaging analysis and data augmentation strategies.
