Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder

Accurate segmentation of anatomical structures in chest radiographs is essential for many computer-aided diagnosis tasks. In this paper we investigate the latest fully-convolutional architectures for the task of multi-class segmentation of the lungs field, heart and clavicles in a chest radiograph. In addition, we explore the influence of using different loss functions in the training process of a neural network for semantic segmentation. We evaluate all models on a common benchmark of 247 X-ray images from the JSRT database and ground-truth segmentation masks from the SCR dataset. Our best performing architecture, is a modified U-Net that benefits from pre-trained encoder weights. This model outperformed the current state-of-the-art methods tested on the same benchmark, with Jaccard overlap scores of 96.1% for lung fields, 90.6% for heart and 85.5% for clavicles.Comment: Presented at the First International Workshop on Thoracic Image Analysis (TIA), MICCAI 201

Why are structured data different? Relating differences in data representation to the rationale of OpenSDE

OpenSDE is an application that supports clinicians with structured recording of narrative patient data to enable use of data in both clinical practice and research. OpenSDE is based on a rationale and requirements for structured data entry. In this study, we analyse the impact of the rationale and the requirements on data representation using OpenSDE. Three paediatricians transcribed 20 paper patient records using OpenSDE. The transcribed records were compared; the findings that were the same in content but differed in representation (e.g. recorded as free text instead of in a structured manner) were categorized in one of three categories of difference in representation. The transcribed records contained 1764 findings in total. The medical content of 302 of these findings was represented differently by at least one clinician and was thus included in this study. In OpenSDE, clinicians are free to determine the degree of detail at which patient data are described. This flexibility accounts for 87% of the differences in data representation. Thirteen per cent of the differences are due to clinicians interpreting and translating phrases from the source text and transcribing these to (different) concepts in OpenSDE. The differences in data representation largely result from initial design decisions for OpenSDE

Distribution of emphysema in heavy smokers: Impact on pulmonary function

SummaryPurposeTo investigate impact of distribution of computed tomography (CT) emphysema on severity of airflow limitation and gas exchange impairment in current and former heavy smokers participating in a lung cancer screening trial.Materials and MethodsIn total 875 current and former heavy smokers underwent baseline low-dose CT (30mAs) in our center and spirometry and diffusion capacity testing on the same day as part of the Dutch–Belgian Lung Cancer Screening Trial (NELSON). Emphysema was quantified for 872 subjects as the number of voxels with an apparent lowered X-ray attenuation coefficient. Voxels attenuated <−950HU were categorized as representing severe emphysema (ES950), while voxels attenuated between −910HU and −950HU represented moderate emphysema (ES910). Impact of distribution on severity of pulmonary function impairment was investigated with logistic regression, adjusted for total amount of emphysema.ResultsFor ES910 an apical distribution was associated with more airflow obstruction and gas exchange impairment than a basal distribution (both p<0.01). The FEV1/FVC ratio was 1.6% (95% CI 0.42% to 2.8%) lower for apical predominance than for basal predominance, for Tlco/VA the difference was 0.12% (95% CI 0.076–0.15%). Distribution of ES950 had no impact on FEV1/FVC ratio, while an apical distribution was associated with a 0.076% (95% CI 0.038–0.11%) lower Tlco/VA (p<0.001).ConclusionIn a heavy smoking population, an apical distribution is associated with more severe gas exchange impairment than a basal distribution; for moderate emphysema it is also associated with a lower FEV1/FVC ratio. However, differences are small, and likely clinically irrelevant

Structured data entry for narrative data in a broad specialty: patient history and physical examination in pediatrics.

BACKGROUND: Whereas an electronic medical record (EMR) system can partly address the limitations, of paper-based documentation, such as fragmentation of patient data, physical paper records missing and poor legibility, structured data entry (SDE, i.e. data entry based on selection of predefined medical concepts) is essential for uniformity of data, easier reporting, decision support, quality assessment, and patient-oriented clinical research. The aim of this project was to explore whether a previously developed generic (i.e. content independent) SDE application to support the structured documentation of narrative data (called OpenSDE) can be used to model data obtained at history taking and physical examination of a broad specialty. METHODS: OpenSDE was customized for the broad domain of general pediatrics: medical concepts and its descriptors from history taking and physical examination were modeled into a tree structure. RESULTS: An EMR system allowing structured recording (OpenSDE) of pediatric narrative data was developed. Patient history is described by 20 main concepts and physical examination by 11. In total, the thesaurus consists of about 1800 items, used in 8648 nodes in the tree with a maximum depth of 9 levels. Patient history contained 6312 nodes, and physical examination 2336. User-defined entry forms can be composed according to individual needs, without affecting the underlying data representation. The content of the tree can be adjusted easily and sharing records among different disciplines is possible. Data that are relevant in more than one context can be accessed from multiple branches of the tree without duplication or ambiguity of data entry via "shortcuts". CONCLUSION: An expandable EMR system with structured data entry (OpenSDE) for pediatrics was developed, allowing structured documentation of patient history and physical examination. For further evaluation in other environments, the tree structure for general pediatrics is available at the Erasmus MC Web site (in Dutch, translation into English in progress) 1. The generic OpenSDE application is available at the OpenSDE Web site 2

Thermal shock fragmentation of Mg silicates within scoriaceous micrometeorites reveal hydrated asteroidal sources

Scoriaceous micrometeorites are highly vesicular extraterrestrial dust particles that have experienced partial melting during atmospheric entry. We report the occurrence of clusters of anhedral relict forsterite crystals within these particles that testify to in situ fragmentation. The absence of similar clusters within unmelted micrometeorites suggests that fragmentation occurs during atmospheric entry rather than by parent body shock reprocessing. Clusters of broken forsterite crystals are shown to form as a result of fracturing owing to thermal stress developed during entry heating and require thermal gradients of >200 K µm–1 in order for differential thermal expansion to exceed the critical shear strength of olivine. Thermal gradients of this magnitude significantly exceed those resulting from thermal conduction and require the endothermic decomposition of phyllosilicates. Fragmented relict forsterite within scoriaceous micrometeorites, therefore, indicate that the precursor grains were similar to CI and CM2 chondrites and retained phyllosilicate prior to atmospheric entry and thus were not dehydrated on the parent asteroid by shock or thermal metamorphism. Explosive fragmentation of hydrous asteroids during collisions, therefore, does not significantly bias the interplanetary dust population

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