Image analysis for moving organ, breast, and thoracic images : third International Workshop, RAMBO 2018, fourth International Workshop, BIA 2018, and first International Workshop, TIA 2018, held in conjunction with MICCAI 2018, Granada,...最新文献

Image registration is a well-known problem in the field of medical imaging. In this paper, we focus on the registration of chest inspiratory and expiratory computed tomography (CT) scans from the same patient. Our method recovers the diffeomorphic elastic displacement vector field (DVF) by jointly regressing the direct and the inverse transformation. Our architecture is based on the RegNet network but we implement a reinforced learning strategy that can accommodate a large training dataset. Our results show that our method performs with a lower estimation error for the same number of epochs than the RegNet approach.

In recent years, the ability to accurately measuring and analyzing the morphology of small pulmonary structures on chest CT images, such as airways, is becoming of great interest in the scientific community. As an example, in COPD the smaller conducting airways are the primary site of increased resistance in COPD, while small changes in airway segments can identify early stages of bronchiectasis. To date, different methods have been proposed to measure airway wall thickness and airway lumen, but traditional algorithms are often limited due to resolution and artifacts in the CT image. In this work, we propose a Convolutional Neural Regressor (CNR) to perform cross-sectional measurements of airways, considering wall thickness and airway lumen at once. To train the networks, we developed a generative synthetic model of airways that we refined using a Simulated and Unsupervised Generative Adversarial Network (SimGAN). We evaluated the proposed method by first computing the relative error on a dataset of synthetic images refined with SimGAN, in comparison with other methods. Then, due to the high complexity to create an in-vivo ground-truth, we performed a validation on an airway phantom constructed to have airways of different sizes. Finally, we carried out an indirect validation analyzing the correlation between the percentage of the predicted forced expiratory volume in one second (FEV1%) and the value of the Pi10 parameter. As shown by the results, the proposed approach paves the way for the use of CNNs to precisely and accurately measure small lung airways with high accuracy.

The characterization of the vasculature in the mediastinum, more specifically the pulmonary artery, is of vital importance for the evaluation of several pulmonary vascular diseases. Thus, the goal of this study is to automatically segment the pulmonary artery (PA) from computed tomography angiography images, which opens up the opportunity for more complex analysis of the evolution of the PA geometry in health and disease and can be used in complex fluid mechanics models or individualized medicine. For that purpose, a new 3D convolutional neural network architecture is proposed, which is trained on images coming from different patient cohorts. The network makes use a strong data augmentation paradigm based on realistic deformations generated by applying principal component analysis to the deformation fields obtained from the affine registration of several datasets. The network is validated on 91 datasets by comparing the automatic segmentations with semi-automatically delineated ground truths in terms of mean Dice and Jaccard coefficients and mean distance between surfaces, which yields values of 0.89, 0.80 and 1.25 mm, respectively. Finally, a comparison against a Unet architecture is also included.

Labeled data is the current bottleneck of medical image research. Substantial efforts are made to generate segmentation masks to characterize a given organ. The community ends up with multiple label maps of individual structures in different cases, not suitable for current multi-organ segmentation frameworks. Our objective is to leverage segmentations from multiple organs in different cases to generate a robust multi-organ deep learning segmentation network. We propose a modified cost-function that takes into account only the voxels labeled in the image, ignoring unlabeled structures. We evaluate the proposed methodology in the context of pectoralis muscle and subcutaneous fat segmentation on chest CT scans. Six different structures are segmented from an axial slice centered on the transversal aorta. We compare the performance of a network trained on 3,000 images where only one structure has been annotated (PUNet) against six UNets (one per structure) and a multi-class UNet trained on 500 completely annotated images, showing equivalence between the three methods (Dice coefficients of 0.909, 0.906 and 0.909 respectively). We further propose a modification of the architecture by adding convolutions to the skip connections (CUNet). When trained with partially labeled images, it outperforms statistically significantly the other three methods (Dice 0.916, p< 0.0001). We, therefore, show that (a) when keeping the number of organ annotation constant, training with partially labeled images is equivalent to training with wholly labeled data and (b) adding convolutions in the skip connections improves performance.

Recent studies have suggested the central role of small airway destruction in the pathogenesis of COPD leading to further parenchymal destruction. This evidence has sparked the interest in in-vivo assessment of small airway disease overall at the early onset of the disease. The parametric response mapping (PRM) technique has been proposed to distinguish gas trapping due to small airway disease from low attenuation areas due to emphysema. Despite its success, the PRM technique shows some limitations that are precluding the interpretation of its results. The density value used to assess gas trapping highly depends on acquisition parameters, such as dose and reconstruction kernel, and changes in body size, that introduce inhomogeneous photon absorption patterns. In particular, many studies using PRM employ inspiratory and expiratory images that are obtained at different dose levels. Emphysema impact in early disease may be confounded with the gas trapping due to the noise introduced by differences in the acquisition during the PRM. In this work, we propose a CT harmonization technique to remove the nuisance factors to distinguish between small airway disease and emphysema. Our results show that the measurements based on CT harmonization provide an increase in the detection of both emphysema and airway disease, resulting in a statistically significant impact of both components and a better association with lung function measures.

In this work, we evaluate the relevance of the choice of loss function in the regression of the Agatston score from 3D heart volumes obtained from non-contrast non-ECG gated chest computed tomography scans. The Agatston score is a well-established metric of cardiovascular disease, where an index of coronary artery disease (CAD) is computed by segmenting the calcifications of the arteries and multiplying each calcification by a factor related to their intensity and their volume, creating a final aggregated index. Recent work has automated such task with deep learning techniques, even skipping the segmentation step and performing a direct regression of the Agatston score. We study the effect of the choice of the loss function in such methodologies. We use a large database of 6983 CT scans to which the Agatston score has been manually computed. The dataset is split into a training set and a validation set of n = 1000. We train a deep learning regression network using such data with different loss functions while keeping the structure of the network and training parameters constant. Pearson correlation coefficient ranges from 0.902 to 0.938 depending on the loss function. Correct risk group assignment measurements range between 59.5% and 81.7%. There is a trade-off between the accuracy of the Pearson correlation coefficient and the risk group measurement, which leads to optimize for one or the other.