Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.Rudyanto, RD.; Kerkstra, S.; Van Rikxoort, EM.; Fetita, C.; Brillet, P.; Lefevre, C.; Xue, W.... (2014). Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study. Medical Image Analysis. 18(7):1217-1232. doi:10.1016/j.media.2014.07.003S1217123218

Dysglycemias in pregnancy: from diagnosis to treatment. Brazilian consensus statement

There is an urgent need to find consensus on screening, diagnosing and treating all degrees of DYSGLYCEMIA that may occur during pregnancies in Brazil, considering that many cases of DYSGLYCEMIA in pregnant women are currently not diagnosed, leading to maternal and fetal complications. For this reason the Brazilian Diabetes Society (SBD) and the Brazilian Federation of Gynecology and Obstetrics Societies (FEBRASGO), got together to introduce this proposal. We present here a joint consensus regarding the standardization of clinical management for pregnant women with any degree of Dysglycemia, on the basis of current information, to improve medical assistance and to avoid related complications of Dysglycemia in pregnancy to the mother and the fetus. This consensus aims to standardize the diagnosis among general practitioners, endocrinologists and obstetricians allowing the dissemination of information in basic health units, public and private services, that are responsible for screening, diagnosing and treating disglycemic pregnant patients

Three-dimensional reconstruction of human bronchial tree in HRCT

This paper addresses the issue of 3-D human bronchial tree reconstruction from 2-D segmented slices. The 2-D greylevel slices to be segmented are reconstructed from volumetric data acquired by using a high resolution computerized tomography (HRCT) system operating in spiral mode. The proposed 3-D reconstruction technique consists in three major steps: (1) a fully-automated 2-D segmentation of bronchi performed on each slice and based on mathematical morphology theory; (2) a 3-D oriented and multivalued structure construction characterizing the 3-D topology of the segmented volume and a 3-D oriented propagation based on this structure; and (3) the 3-D reconstruction by using a 3-D mesh technique. Results are presented and discussed. Keywords: 3-D reconstruction, mathematical morphology, connection cost, bronchial tree 1. INTRODUCTION Respiratory diseases are widely spread a#ections, localized mainly in industrial areas and urban agglomerations, and constitute an increasing preoccupati..

Development and Analysis of Patient-Based Complete Conducting Airways Models.

The analysis of high-resolution computed tomography (CT) images of the lung is dependent on inter-subject differences in airway geometry. The application of computational models in understanding the significance of these differences has previously been shown to be a useful tool in biomedical research. Studies using image-based geometries alone are limited to the analysis of the central airways, down to generation 6-10, as other airways are not visible on high-resolution CT. However, airways distal to this, often termed the small airways, are known to play a crucial role in common airway diseases such as asthma and chronic obstructive pulmonary disease (COPD). Other studies have incorporated an algorithmic approach to extrapolate CT segmented airways in order to obtain a complete conducting airway tree down to the level of the acinus. These models have typically been used for mechanistic studies, but also have the potential to be used in a patient-specific setting. In the current study, an image analysis and modelling pipeline was developed and applied to a number of healthy (n = 11) and asthmatic (n = 24) CT patient scans to produce complete patient-based airway models to the acinar level (mean terminal generation 15.8 ± 0.47). The resulting models are analysed in terms of morphometric properties and seen to be consistent with previous work. A number of global clinical lung function measures are compared to resistance predictions in the models to assess their suitability for use in a patient-specific setting. We show a significant difference (p < 0.01) in airways resistance at all tested flow rates in complete airway trees built using CT data from severe asthmatics (GINA 3-5) versus healthy subjects. Further, model predictions of airways resistance at all flow rates are shown to correlate with patient forced expiratory volume in one second (FEV1) (Spearman ρ = -0.65, p < 0.001) and, at low flow rates (0.00017 L/s), FEV1 over forced vital capacity (FEV1/FVC) (ρ = -0.58, p < 0.001). We conclude that the pipeline and anatomical models can be used directly in mechanistic modelling studies and can form the basis for future patient-based modelling studies