Deep Learning from Label Proportions for Emphysema Quantification

We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue. These proportions were visually estimated by experts using a standard grading system, in which grades correspond to intervals (label example: 1-5% of diseased tissue). The proposed architecture encodes the knowledge that the labels represent a volumetric proportion. A custom loss is designed to learn with intervals. Thus, during training, our network learns to segment the diseased tissue such that its proportions fit the ground truth intervals. Our architecture and loss combined improve the performance substantially (8% ICC) compared to a more conventional regression network. We outperform traditional lung densitometry and two recently published methods for emphysema quantification by a large margin (at least 7% AUC and 15% ICC), and achieve near-human-level performance. Moreover, our method generates emphysema segmentations that predict the spatial distribution of emphysema at human level.Comment: Accepted to MICCAI 201

Tree-space statistics and approximations for large-scale analysis of anatomical trees

Statistical analysis of anatomical trees is hard to perform due to differences in the topological structure of the trees. In this paper we define statistical properties of leaf-labeled anatomical trees with geometric edge attributes by considering the anatomical trees as points in the geometric space of leaf-labeled trees. This tree-space is a geodesic metric space where any two trees are connected by a unique shortest path, which corresponds to a tree deformation. However, tree-space is not a manifold, and the usual strategy of performing statistical analysis in a tangent space and projecting onto tree-space is not available. Using tree-space and its shortest paths, a variety of statistical properties, such as mean, principal component, hypothesis testing and linear discriminant analysis can be defined. For some of these properties it is still an open problem how to compute them; others (like the mean) can be computed, but efficient alternatives are helpful in speeding up algorithms that use means iteratively, like hypothesis testing. In this paper, we take advantage of a very large dataset (N = 8016) to obtain computable approximations, under the assumption that the data trees parametrize the relevant parts of tree-space well. Using the developed approximate statistics, we illustrate how the structure and geometry of airway trees vary across a population and show that airway trees with Chronic Obstructive Pulmonary Disease come from a different distribution in tree-space than healthy ones. Software is available from http://image.diku.dk/aasa/software.php

Towards automatic pulmonary nodule management in lung cancer screening with deep learning

The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.Comment: Published on Scientific Report

Chest computed tomography features of heart failure:A prospective observational study in patients with acute dyspnea

BACKGROUND: Pulmonary congestion is a key component of heart failure (HF) that chest computed tomography (CT) can detect. However, no guideline describes which of many anticipated CT signs are most associated with HF in patients with undifferentiated dyspnea. METHODS: In a prospective observational single-center study, we included consecutive patients ≥ 50 years admitted with acute dyspnea to the emergency department. Patients underwent immediate clinical examination, blood sampling, echocardiography, and CT. Two radiologists independently evaluated all images. Acute HF (AHF) was adjudicated by an expert panel blinded to radiology images. LASSO and logistic regression identified the independent CT signs of AHF. RESULTS: Among 232 patients, 102 (44%) had AHF. Of 18 examined CT signs, 5 were associated with AHF (multivariate odds ratio, 95% confidence interval): enlarged heart (20.38, 6.86–76.16), bilateral interlobular thickening (11.67, 1.78–230.99), bilateral pleural effusion (6.39, 1.98–22.85), and increased vascular diameter (4.49, 1.08–33.92). Bilateral ground-glass opacification (2.07, 0.95–4.52) was a consistent fifth essential sign, although it was only significant in univariate analysis. Eighty-eight (38%) patients had none of the five CT signs corresponding to a 68% specificity and 86% sensitivity for AHF, while two or more of the five CT signs occurred in 68 (29%) patients, corresponding to 97% specificity and 67% sensitivity. A weighted score based on these five CT signs had an 0.88 area under the curve to detect AHF. CONCLUSIONS: Five CT signs seem sufficient to assess the risk of AHF in the acute setting. The absence of these signs indicates a low probability, one sign makes AHF highly probable, and two or more CT signs mean almost certain AHF

Chest computed tomography features of heart failure: A prospective observational study in patients with acute dyspnea

Background: Pulmonary congestion is a key component of heart failure (HF) that chest computed tomography (CT) can detect. However, no guideline describes which of many anticipated CT signs are most associated with HF in patients with undifferentiated dyspnea.Methods: In a prospective observational single-center study, we included consecutive patients ≥ 50 years admitted with acute dyspnea to the emergency department. Patients underwent immediate clinical examination, blood sampling, echocardiography, and CT. Two radiologists independently evaluated all images. Acute HF (AHF) was adjudicated by an expert panel blinded to radiology images. LASSO and logistic regression identified the independent CT signs of AHF.Results: Among 232 patients, 102 (44%) had AHF. Of 18 examined CT signs, 5 were associated with AHF (multivariate odds ratio, 95% confidence interval): enlarged heart (20.38, 6.86–76.16), bilateral interlobular thickening (11.67, 1.78–230.99), bilateral pleural effusion (6.39, 1.98–22.85), and increased vascular diameter (4.49, 1.08–33.92). Bilateral ground-glass opacification (2.07, 0.95–4.52) was a consistent fifth essential sign, although it was only significant in univariate analysis. Eighty-eight (38%) patients had none of the five CT signs corresponding to a 68% specificity and 86% sensitivity for AHF, while two or more of the five CT signs occurred in 68 (29%) patients, corresponding to 97% specificity and 67% sensitivity. A weighted score based on these five CT signs had an 0.88 area under the curve to detect AHF.Conclusions: Five CT signs seem sufficient to assess the risk of AHF in the acute setting. The absence of these signs indicates a low probability, one sign makes AHF highly probable, and two or more CT signs mean almost certain AHF