Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies

To develop a fully automatic model capable of reliably quantifying epicardial adipose tissue (EAT) volumes and attenuation in large scale population studies to investigate their relation to markers of cardiometabolic risk. Non-contrast cardiac CT images from the SCAPIS study were used to train and test a convolutional neural network based model to quantify EAT by: segmenting the pericardium, suppressing noise-induced artifacts in the heart chambers, and, if image sets were incomplete, imputing missing EAT volumes. The model achieved a mean Dice coefficient of 0.90 when tested against expert manual segmentations on 25 image sets. Tested on 1400 image sets, the model successfully segmented 99.4% of the cases. Automatic imputation of missing EAT volumes had an error of less than 3.1% with up to 20% of the slices in image sets missing. The most important predictors of EAT volumes were weight and waist, while EAT attenuation was predicted mainly by EAT volume. A model with excellent performance, capable of fully automatic handling of the most common challenges in large scale EAT quantification has been developed. In studies of the importance of EAT in disease development, the strong co-variation with anthropometric measures needs to be carefully considered

Artificial intelligence-based measurements of PET/CT imaging biomarkers are associated with disease-specific survival of high-risk prostate cancer patients

Objective: Artificial intelligence (AI) offers new opportunities for objective quantitative measurements of imaging biomarkers from positron-emission tomography/computed tomography (PET/CT). Clinical image reporting relies predominantly on observer-dependent visual assessment and easily accessible measures like SUVmax, representing lesion uptake in a relatively small amount of tissue. Our hypothesis is that measurements of total volume and lesion uptake of the entire tumour would better reflect the disease`s activity with prognostic significance, compared with conventional measurements. Methods: An AI-based algorithm was trained to automatically measure the prostate and its tumour content in PET/CT of 145 patients. The algorithm was then tested retrospectively on 285 high-risk patients, who were examined using 18F-choline PET/CT for primary staging between April 2008 and July 2015. Prostate tumour volume, tumour fraction of the prostate gland, lesion uptake of the entire tumour, and SUVmax were obtained automatically. Associations between these measurements, age, PSA, Gleason score and prostate cancer-specific survival were studied, using a Cox proportional-hazards regression model. Results: Twenty-three patients died of prostate cancer during follow-up (median survival 3.8 years). Total tumour volume of the prostate (p = 0.008), tumour fraction of the gland (p = 0.005), total lesion uptake of the prostate (p = 0.02), and age (p = 0.01) were significantly associated with disease-specific survival, whereas SUVmax (p = 0.2), PSA (p = 0.2), and Gleason score (p = 0.8) were not. Conclusion: AI-based assessments of total tumour volume and lesion uptake were significantly associated with disease-specific survival in this patient cohort, whereas SUVmax and Gleason scores were not. The AI-based approach appears well-suited for clinically relevant patient stratification and monitoring of individual therapy

Deep learning-based quantification of PET/CT prostate gland uptake: association with overall survival

Aim: To validate a deep-learning (DL) algorithm for automated quantification of prostate cancer on positron emission tomography/computed tomography (PET/CT) and explore the potential of PET/CT measurements as prognostic biomarkers. Material and methods: Training of the DL-algorithm regarding prostate volume was performed on manually segmented CT images in 100 patients. Validation of the DL-algorithm was carried out in 45 patients with biopsy-proven hormone-na\uefve prostate cancer. The automated measurements of prostate volume were compared with manual measurements made independently by two observers. PET/CT measurements of tumour burden based on volume and SUV of abnormal voxels were calculated automatically. Voxels in the co-registered 18F-choline PET images above a standardized uptake value (SUV) of 2\ub765, and corresponding to the prostate as defined by the automated segmentation in the CT images, were defined as abnormal. Validation of abnormal voxels was performed by manual segmentation of radiotracer uptake. Agreement between algorithm and observers regarding prostate volume was analysed by S\uf8rensen-Dice index (SDI). Associations between automatically based PET/CT biomarkers and age, prostate-specific antigen (PSA), Gleason score as well as overall survival were evaluated by a univariate Cox regression model. Results: The SDI between the automated and the manual volume segmentations was 0\ub778 and 0\ub779, respectively. Automated PET/CT measures reflecting total lesion uptake and the relation between volume of abnormal voxels and total prostate volume were significantly associated with overall survival (P\ua0=\ua00\ub702), whereas age, PSA, and Gleason score were not. Conclusion: Automated PET/CT biomarkers showed good agreement to manual measurements and were significantly associated with overall survival

Automated estimation of in-plane nodule shape in chest tomosynthesis images

The purpose of this study was to develop an automated segmentation method for lung nodules in chest tomo-synthesis images. A number of simulated nodules of different sizes and shapes were created and inserted in two different locations into clinical chest tomosynthesis projections. The tomosynthesis volumes were then reconstructed using standard cone beam filtered back projection, with 1 mm slice interval. For the in-plane segmentation, the central plane of each nodule was selected. The segmentation method was formulated as an optimization problem where the nodule boundary corresponds to the minimum of the cost function, which is found by dynamic programming. The cost function was composed of terms related to pixel intensities, edge strength, edge direction and a smoothness constraint. The segmentation results were evaluated using an overlap measure (Dice index) of nodule regions and a distance measure (Hausdorff distance) between true and segmented nodule. On clinical images, the nodule segmentation method achieved a mean Dice index of 0.96 \ub1 0.01, and a mean Hausdorff distance of 0.5 \ub1 0.2 mm for isolated nodules and for nodules close to other lung structures a mean Dice index of 0.95 \ub1 0.02 and a mean Hausdorff distance of 0.5 \ub1 0.2 mm. The method achieved an acceptable accuracy and may be useful for area estimation of lung nodules

An attempt to estimate out-of-plane lung nodule elongation in tomosynthesis images

In chest tomosynthesis (TS) the most commonly used reconstruction methods are based on Filtered Back Projection (FBP) algorithms. Due to the limited angular range of x-ray projections, FBP reconstructed data is typically associated with a low spatial resolution in the out-of-plane dimension. Lung nodule measures that depend on depth information such as 3D shape and volume are therefore difficult to estimate. In this paper the relation between features from FBP reconstructed lung nodules and the true out-of-plane nodule elongation is investigated and a method for estimating the out-of-plane nodule elongation is proposed. In order to study these relations a number of steps that include simulation of spheroidal-shaped nodules, insertion into synthetic data volumes, construction of TS-projections and FBP-reconstruction were performed. In addition, the same procedure was used to simulate nodules and insert them into clinical chest TS projection data. The reconstructed nodule data was then investigated with respect to in-plane diameter, out-of-plane elongation, and attenuation coefficient. It was found that the voxel value in each nodule increased linearly with nodule elongation, for nodules with a constant attenuation coefficient. Similarly, the voxel value increased linearly with in-plane diameter. These observations indicate the possibility to predict the nodule elongation from the reconstructed voxel intensity values. Such a method would represent a quantitative approach to chest tomosynthesis that may be useful in future work on volume and growth rate estimation of lung nodules

Image Fusion of Reconstructed Digital Tomosynthesis Volumes From a Frontal and a Lateral Acquisition

Digital tomosynthesis (DTS) has been used in chest imaging as a low radiation dose alternative to computed tomography (CT). Traditional DTS shows limitations in the spatial resolution in the out-of-plane dimension. As a first indication of whether a dual-plane dual-view (DPDV) DTS data acquisition can yield a fair resolution in all three spatial dimensions, a manual registration between a frontal and a lateral image volume was performed. An anthropomorphic chest phantom was scanned frontally and laterally using a linear DTS acquisition, at 120 kVp. The reconstructed image volumes were resampled and manually co-registered. Expert radiologist delineations of the mediastinal soft tissues enabled calculation of similarity metrics in regard to delineations in a reference CT volume. The fused volume produced the highest total overlap, implying that the fused volume was a more isotropic 3D representation of the examined object than the traditional chest DTS volumes