Objectives: Bone segmentation can help bone disease diagnosis or post treatment assessment but manual segmentation is a time consuming and tedious task in clinical practice. In this work, three automatic methods to segment bone structures on whole body CT images were compared. Methods: A threshold-based approach with morphological operations and two deep learning methods using a 3D U-Net with different losses, one with a cross entropy/Dice loss and the second with a Hausdorff Distance/Dice loss, were developed. Ground truth bone segmentations were generated by manually correcting the results obtained with the threshold based method. The automatic bone segmentations were evaluated using a Dice score and Hausdorff distance. Visual evaluation was also performed by a medical expert. Results: Dice scores of 0.953, 0.986 and 0.978 were achieved for the Threshold-based method and the two deep learning methods, respectively. Visual evaluation showed that the deep learning method with a Hausdorff Distance/Dice loss performed the best.
The study aims to assess the performance in differentiating benign from malignant kidney masses using a radiomics approach. For this retrospective study we worked with the scans of 210 patients from the publicly available KiTS19 dataset. Each scan had segmentations of the healthy kidney tissue, benign lesions and malignant tumors. In Phase 1 of our study, we reduced the number of radiomic features (105) extracted from the scans by using four feature selection and ranking algorithms: recursive feature elimination (RFE), fisher score, partial least square discriminant analysis (PLS-DA) and linear support vector machine (l-SVM). The features selected by each method were then used to train a series of random forest (RF) classifiers. In Phase 2, we trained a convolutional neural network (CNN) to automatically perform the segmentation of benign and malignant kidney masses. We then placed the best performing RF classifier from Phase 1 in series with the CNN to see if it corrected its prediction. The best classification performance was obtained when training a RF classifier with the 8 features selected by the RFE method (accuracy: 0.974). This RF model applied to the segmentations derived from the neural network improved the CNN’s overall results: the dice score for malignant mass went from 0.74 to 0.79 and dice score for benign mass from 0.55 to 0.80. The studied radiomics approach proved to be an accurate solution to classify benign and malignant kidney masses. A deep learning algorithm has shown to also benefit from its predictive power.
Cross-modality synthesis represent nowadays a promising application in medical image processing to manage the problem of paired data scarcity. In this work we designed and trained a CycleGAN model to generate PET/CT data from 2D slices collected from the liver body region of twelve patients. The results obtained from the six test patients show how our model can outperform baseline CycleGAN framework and effectively be used for synthesizing artificial images to be used for data augmentation or dataset completion.
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