Segmentation of kidney and renal collecting system on 3D computed tomography images

Surgical training for minimal invasive kidney interventions (MIKI) has huge importance within the urology field. Within this topic, simulate MIKI in a patient-specific virtual environment can be used for pre-operative planning using the real patient's anatomy, possibly resulting in a reduction of intra-operative medical complications. However, the validated VR simulators perform the training in a group of standard models and do not allow patient-specific training. For a patient-specific training, the standard simulator would need to be adapted using personalized models, which can be extracted from pre-operative images using segmentation strategies. To date, several methods have already been proposed to accurately segment the kidney in computed tomography (CT) images. However, most of these works focused on kidney segmentation only, neglecting the extraction of its internal compartments. In this work, we propose to adapt a coupled formulation of the B-Spline Explicit Active Surfaces (BEAS) framework to simultaneously segment the kidney and the renal collecting system (CS) from CT images. Moreover, from the difference of both kidney and CS segmentations, one is able to extract the renal parenchyma also. The segmentation process is guided by a new energy functional that combines both gradient and region-based energies. The method was evaluated in 10 kidneys from 5 CT datasets, with different image properties. Overall, the results demonstrate the accuracy of the proposed strategy, with a Dice overlap of 92.5%, 86.9% and 63.5%, and a point-to-surface error around 1.6 mm, 1.9 mm and 4 mm for the kidney, renal parenchyma and CS, respectively.NORTE-01-0145-FEDER0000I3, and NORTE-01-0145-FEDER-024300, supported by Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER), and also been funded by FEDER funds, through Competitiveness Factors Operational Programme (COMPETE), and by national funds, through the FCT-Fundacao para a Ciência e Tecnologia, under the scope of the project POCI-01-0145-FEDER-007038. The authors acknowledge FCT-Fundação para a Ciância e a Tecnologia, Portugal, and the European Social Found, European Union, for funding support through the Programa Operacional Capital Humano (POCH).info:eu-repo/semantics/publishedVersio

3D Kidney Segmentation from Abdominal Images Using Spatial-Appearance Models

Kidney segmentation is an essential step in developing any noninvasive computer-assisted diagnostic system for renal function assessment. This paper introduces an automated framework for 3D kidney segmentation from dynamic computed tomography (CT) images that integrates discriminative features from the current and prior CT appearances into a random forest classification approach. To account for CT images’ inhomogeneities, we employ discriminate features that are extracted from a higher-order spatial model and an adaptive shape model in addition to the first-order CT appearance. To model the interactions between CT data voxels, we employed a higher-order spatial model, which adds the triple and quad clique families to the traditional pairwise clique family. The kidney shape prior model is built using a set of training CT data and is updated during segmentation using not only region labels but also voxels’ appearances in neighboring spatial voxel locations. Our framework performance has been evaluated on in vivo dynamic CT data collected from 20 subjects and comprises multiple 3D scans acquired before and after contrast medium administration. Quantitative evaluation between manually and automatically segmented kidney contours using Dice similarity, percentage volume differences, and 95th-percentile bidirectional Hausdorff distances confirms the high accuracy of our approach

Sparse feature learning for image analysis in segmentation, classification, and disease diagnosis.

The success of machine learning algorithms generally depends on intermediate data representation, called features that disentangle the hidden factors of variation in data. Moreover, machine learning models are required to be generalized, in order to reduce the specificity or bias toward the training dataset. Unsupervised feature learning is useful in taking advantage of large amount of unlabeled data, which is available to capture these variations. However, learned features are required to capture variational patterns in data space. In this dissertation, unsupervised feature learning with sparsity is investigated for sparse and local feature extraction with application to lung segmentation, interpretable deep models, and Alzheimer\u27s disease classification. Nonnegative Matrix Factorization, Autoencoder and 3D Convolutional Autoencoder are used as architectures or models for unsupervised feature learning. They are investigated along with nonnegativity, sparsity and part-based representation constraints for generalized and transferable feature extraction