Use of Variational Autoencoders with Unsupervised Learning to Detect Incorrect Organ Segmentations on CT
Veit Sandfort, Ke Yan, Peter M. Graffy, Perry J. Pickhardt, Ronald M. Summers
Purpose: To develop a deep learning model to detect incorrect organ segmentations on CT.
Materials and Methods: In this retrospective study, a deep learning method was developed using variational autoencoders to identify problematic organ segmentations. First, three different three-dimensional UNets were trained on segmented CT images of liver (n = 141), spleen (n = 51), and kidney (n = 66). Next, a total of 12 945 CT images were segmented by the 3D UNets and output segmentations were used to train three different variational autoencoders (VAE) for the detection of problematic segmentations. Automatic reconstruction errors (Dice scores) were then calculated. A random sampling of 2510 segmented images for each the liver, spleen, and kidney models were assessed manually by a human reader to determine problematic and correct segmentations. The ability of the VAEs to identify unusual or problematic segmentations was evaluated using receiver operator characteristic curve analysis and compared with traditional non-deep-learning methods for outlier detection. Using the VAE outputs, passive and active learning approaches were performed on the original 3D UNets to determine if training could decrease segmentation error rates (15 CT scans were added to the original training data, according to each approach).
Results: The area under the receiver operating characteristic curve (AUC) for detecting problematic segmentations using the VAE method was 0.90 (95% CI: 0.89, 0.92) for kidney, 0.94 (95% CI: 0.93, 0.95) for liver, and 0.81 (95% CI: 0.80, 0.82) for spleen. The VAE performance was higher compared with traditional methods in most cases, for example for liver segmentation the highest performing nondeep learning method for outlier detection had an AUC of 0.83 (95% CI: 0.77, 0.90) compared with 0.94 (95% CI: 0.93, 0.95) using the VAE method (P < .05). Using the information on problematic segmentations for active learning approaches decreased 3D UNet segmentation error rates (original error rate: 7.1%, passive learning: 6.0%, and active learning, 5.7%).
Conclusion: A method was developed to screen for unusual and problematic automatic organ segmentations using a 3D VAE.
Read Full Article Here: https://doi.org/10.1148/ryai.2021200218