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Unsupervised detection of potentially necrotic intestinal segments using autoencoder residuals and multispectral
Yi Xie1, DanFei Huang1, JiaXuan Yan1
1College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130000, China; Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan 528400, China.
Summary
This study introduces an unsupervised autoencoder residual method for sensitive detection of necrotic intestines during surgery. The approach significantly improves early detection accuracy, aiding surgical decisions.
Area of Science:
- Medical Imaging
- Surgical Technology
- Computational Biology
Background:
- Accurate identification of necrotic intestinal segments is crucial for surgical decision-making.
- Current methods often require labeled data or miss subtle early abnormalities, limiting clinical use.
- Developing unsupervised methods for sensitive anomaly detection is a key challenge.
Purpose of the Study:
- To develop an unsupervised one-class classification method using autoencoder (AE) residuals for sensitive detection of potentially necrotic intestinal segments.
- To amplify subtle spectral differences in multispectral data to improve anomaly detection.
- To evaluate the performance of AE residuals combined with unsupervised classifiers in detecting early signs of intestinal necrosis.
Main Methods:
- Trained an autoencoder (AE) on normal small intestine multispectral data to generate residuals amplifying abnormal spectral features.
- Utilized residual features as input for unsupervised one-class classifiers: Local Outlier Factor, Isolation Forest, and Minimum Covariance Determinant (MCD).
- Validated the method on rabbit models with varying intestinal occlusion durations.
Main Results:
- The AE residual approach significantly enhanced the performance of all tested unsupervised classifiers.
- Early detection (10 min occlusion) achieved ~80% accuracy, with MCD sensitivity increasing from 26.5% to 80.7%.
- Classifier sensitivity positively correlated with occlusion duration, aligning with physiological patterns, and visualization confirmed improved performance and spatial continuity.
Conclusions:
- Unsupervised autoencoder residuals offer a promising approach for sensitive and early detection of necrotic intestinal segments.
- This method overcomes limitations of labeled data dependency and improves detection of subtle abnormalities.
- The technique shows potential for broader applications in medical imaging anomaly detection.
