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Register a new userNovel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning
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Added by
Elsa Angelini
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes previously identified on autopsy. Unsupervised learning of emphysema subtypes on computed tomography (CT) opens the way to new definitions of emphysema subtypes and eliminates the need of thorough manual labeling. However, CT-based emphysema subtypes have been limited to texture-based patterns without considering spatial location. In this work, we introduce a standardized spatial mapping of the lung for quantitative study of lung texture location, and propose a novel framework for combining spatial and texture information to discover spatially-informed lung texture patterns (sLTPs) that represent novel emphysema subtypes. Exploiting two cohorts of full-lung CT scans from the MESA COPD and EMCAP studies, we first show that our spatial mapping enables population-wide study of emphysema spatial location. We then evaluate the characteristics of the sLTPs discovered on MESA COPD, and show that they are reproducible, able to encode standard emphysema subtypes, and associated with physiological symptoms.
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Pulmonary emphysema is traditionally subcategorized into three subtypes, which have distinct radiological appearances on computed tomography (CT) and can help with the diagnosis of chronic obstructive pulmonary disease (COPD). Automated texture-based quantification of emphysema subtypes has been successfully implemented via supervised learning of these three emphysema subtypes. In this work, we demonstrate that unsupervised learning on a large heterogeneous database of CT scans can generate texture prototypes that are visually homogeneous and distinct, reproducible across subjects, and capable of predicting accurately the three standard radiological subtypes. These texture prototypes enable automated labeling of lung volumes, and open the way to new interpretations of lung CT scans with finer subtyping of emphysema.
Purpose: To characterize regional pulmonary function on CT images using a radiomic filtering approach. Methods: We develop a radiomic filtering technique to capture the image encoded regional pulmonary ventilation information on CT. The lung volumes were first segmented on 46 CT images. Then, a 3D sliding window kernel is implemented to map the impulse response of radiomic features. Specifically, for each voxel in the lungs, 53 radiomic features were calculated in such a rotationally-invariant 3D kernel to capture spatially-encoded information. Accordingly, each voxel coordinate is represented as a 53-dimensional feature vector, and each image is represented as an image tensor that we refer to as a feature map. To test the technique as a potential pulmonary biomarker, the Spearman correlation analysis is performed between the feature map and matched nuclear imaging measurements (Galligas PET or DTPA-SPECT) of lung ventilation. Results: Two features were found to be highly correlated with benchmark pulmonary ventilation function results based on the median of Spearman correlation coefficient () distribution. In particular, feature GLRLM-based Run Length Non-uniformity and GLCOM-based Sum Average achieved robust high correlation across 46 patients and both Galligas PET or DTPA-SPECT nuclear imaging modalities, with the range (median) of [0.05, 0.67] (0.46) and [0.21, 0.65] (0.45), respectively. Such results are comparable to other image-based pulmonary function quantification techniques. Conclusions: Our results provide evidence that local regions of sparsely encoded homogenous lung parenchyma on CT are associated with diminished radiotracer uptake and measured lung ventilation defects on PET/SPECT imaging. This finding demonstrates the potential of radiomics to serve as a non-invasive surrogate of regional lung function and provides hypothesis-generating data for future studies.
This paper proposes a novel and efficient method to build a Computer-Aided Diagnoses (CAD) system for lung nodule detection based on Computed Tomography (CT). This task was treated as an Object Detection on Video (VID) problem by imitating how a radiologist reads CT scans. A lung nodule detector was trained to automatically learn nodule features from still images to detect lung nodule candidates with both high recall and accuracy. Unlike previous work which used 3-dimensional information around the nodule to reduce false positives, we propose two simple but efficient methods, Multi-slice propagation (MSP) and Motionless-guide suppression (MLGS), which analyze sequence information of CT scans to reduce false negatives and suppress false positives. We evaluated our method in open-source LUNA16 dataset which contains 888 CT scans, and obtained state-of-the-art result (Free-Response Receiver Operating Characteristic score of 0.892) with detection speed (end to end within 20 seconds per patient on a single NVidia GTX 1080) much higher than existing methods.
Recent literature has shown that features obtained from supervised training of CNNs may over-emphasize texture rather than encoding high-level information. In self-supervised learning in particular, texture as a low-level cue may provide shortcuts that prevent the network from learning higher level representations. To address these problems we propose to use classic methods based on anisotropic diffusion to augment training using images with suppressed texture. This simple method helps retain important edge information and suppress texture at the same time. We empirically show that our method achieves state-of-the-art results on object detection and image classification with eight diverse datasets in either supervised or self-supervised learning tasks such as MoCoV2 and Jigsaw. Our method is particularly effective for transfer learning tasks and we observed improved performance on five standard transfer learning datasets. The large improvements (up to 11.49%) on the Sketch-ImageNet dataset, DTD dataset and additional visual analyses with saliency maps suggest that our approach helps in learning better representations that better transfer.
Modern scientific and technological advances are allowing botanists to use computer vision-based approaches for plant identification tasks. These approaches have their own challenges. Leaf classification is a computer-vision task performed for the automated identification of plant species, a serious challenge due to variations in leaf morphology, including its size, texture, shape, and venation. Researchers have recently become more inclined toward deep learning-based methods rather than conventional feature-based methods due to the popularity and successful implementation of deep learning methods in image analysis, object recognition, and speech recognition. In this paper, a botanists behavior was modeled in leaf identification by proposing a highly-efficient method of maximum behavioral resemblance developed through three deep learning-based models. Different layers of the three models were visualized to ensure that the botanists behavior was modeled accurately. The first and second models were designed from scratch.Regarding the third model, the pre-trained architecture MobileNetV2 was employed along with the transfer-learning technique. The proposed method was evaluated on two well-known datasets: Flavia and MalayaKew. According to a comparative analysis, the suggested approach was more accurate than hand-crafted feature extraction methods and other deep learning techniques in terms of 99.67% and 99.81% accuracy. Unlike conventional techniques that have their own specific complexities and depend on datasets, the proposed method required no hand-crafted feature extraction, and also increased accuracy and distributability as compared with other deep learning techniques. It was further considerably faster than other methods because it used shallower networks with fewer parameters and did not use all three models recurrently.
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