Dataset Information

A graph-cut approach for pulmonary artery-vein segmentation in noncontrast CT images.

ABSTRACT: Lung vessel segmentation has been widely explored by the biomedical image processing community; however, the differentiation of arterial from venous irrigation is still a challenge. Pulmonary artery-vein (AV) segmentation using computed tomography (CT) is growing in importance owing to its undeniable utility in multiple cardiopulmonary pathological states, especially those implying vascular remodelling, allowing the study of both flow systems separately. We present a new framework to approach the separation of tree-like structures using local information and a specifically designed graph-cut methodology that ensures connectivity as well as the spatial and directional consistency of the derived subtrees. This framework has been applied to the pulmonary AV classification using a random forest (RF) pre-classifier to exploit the local anatomical differences of arteries and veins. The evaluation of the system was performed using 192 bronchopulmonary segment phantoms, 48 anthropomorphic pulmonary CT phantoms, and 26 lungs from noncontrast CT images with precise voxel-based reference standards obtained by manually labelling the vessel trees. The experiments reveal a relevant improvement in the accuracy (???20%) of the vessel particle classification with the proposed framework with respect to using only the pre-classification based on local information applied to the whole area of the lung under study. The results demonstrated the accurate differentiation between arteries and veins in both clinical and synthetic cases, specifically when the image quality can guarantee a good airway segmentation, which opens a huge range of possibilities in the clinical study of cardiopulmonary diseases.

SUBMITTER: Jimenez-Carretero D

PROVIDER: S-EPMC7307704 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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A graph-cut approach for pulmonary artery-vein segmentation in noncontrast CT images.

Jimenez-Carretero Daniel D Bermejo-Peláez David D Nardelli Pietro P Fraga Patricia P Fraile Eduardo E San José Estépar Raúl R Ledesma-Carbayo Maria J MJ

Medical image analysis 20181126

Lung vessel segmentation has been widely explored by the biomedical image processing community; however, the differentiation of arterial from venous irrigation is still a challenge. Pulmonary artery-vein (AV) segmentation using computed tomography (CT) is growing in importance owing to its undeniable utility in multiple cardiopulmonary pathological states, especially those implying vascular remodelling, allowing the study of both flow systems separately. We present a new framework to approach th ...[more]

PMID: 30579223

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Project description:ObjectivesTo develop a deep learning-based pulmonary vessel segmentation algorithm (DLVS) from noncontrast chest CT and to investigate its clinical implications in assessing vascular remodeling of chronic obstructive lung disease (COPD) patients.MethodsFor development, 104 pulmonary CT angiography scans (49,054 slices) using a dual-source CT were collected, and spatiotemporally matched virtual noncontrast and 50-keV images were generated. Vessel maps were extracted from the 50-keV images. The 3-dimensional U-Net-based DLVS was trained to segment pulmonary vessels (with a vessel map as the output) from virtual noncontrast images (as the input). For external validation, vendor-independent noncontrast CT images (n = 14) and the VESSEL 12 challenge open dataset (n = 3) were used. For each case, 200 points were selected including 20 intra-lesional points, and the probability value for each point was extracted. For clinical validation, we included 281 COPD patients with low-dose noncontrast CTs. The DLVS-calculated volume of vessels with a cross-sectional area < 5 mm2 (PVV5) and the PVV5 divided by total vessel volume (%PVV5) were measured.ResultsDLVS correctly segmented 99.1% of the intravascular points (1,387/1,400) and 93.1% of the extravascular points (1,309/1,400). The areas-under-the receiver-operating characteristic curve (AUROCs) were 0.977 and 0.969 for the two external validation datasets. For the COPD patients, both PPV5 and %PPV5 successfully differentiated severe patients whose FEV1 < 50 (AUROCs; 0.715 and 0.804) and were significantly correlated with the emphysema index (Ps < .05).ConclusionsDLVS successfully segmented pulmonary vessels on noncontrast chest CT by utilizing spatiotemporally matched 50-keV images from a dual-source CT scanner and showed promising clinical applicability in COPD.Key points• We developed a deep learning pulmonary vessel segmentation algorithm using virtual noncontrast images and 50-keV enhanced images produced by a dual-source CT scanner. • Our algorithm successfully segmented vessels on diseased lungs. • Our algorithm showed promising results in assessing the loss of small vessel density in COPD patients.

Project description:PURPOSE:We evaluated the feasibility of using an automatic segmentation tool to delineate cardiac substructures from noncontrast computed tomography (CT) images for cardiac dosimetry and toxicity analyses for patients with nonsmall cell lung cancer (NSCLC) after radiotherapy. MATERIAL AND METHODS:We used an in-house developed multi-atlas segmentation tool to delineate 11cardiac substructures, including the whole heart, four heart chambers, and six greater vessels, automatically from the averaged 4D-CT planning images of 49 patients with NSCLC. Two experienced radiation oncologists edited the auto-segmented contours. Times for automatic segmentation and modification were recorded. The modified contours were compared with the auto-segmented contours in terms of Dice similarity coefficient (DSC) and mean surface distance (MSD) to evaluate the extent of modification. Differences in dose-volume histogram (DVH) characteristics were also evaluated for the modified versus auto-segmented contours. RESULTS:The mean automatic segmentation time for all 11 structures was 7-9?min. For the 49 patients, the mean DSC values (±SD) ranged from .73?±?.08 to .95?±?.04, and the mean MSD values ranged from 1.3?±?.6?mm to 2.9?±?5.1?mm. Overall, the modifications were small; the largest modifications were in the pulmonary vein and the inferior vena cava. The heart V30 (volume receiving dose ?30?Gy) and the mean dose to the whole heart and the four heart chambers were not different for the modified versus the auto-segmented contours based on the statistically significant condition of p?<?.05. Also, the maximum dose to the great vessels was no different except for the pulmonary vein. CONCLUSIONS:Automatic segmentation of cardiac substructures did not require substantial modifications. Dosimetric evaluation showed no significant difference between the auto-segmented and modified contours for most structures, which suggests that the auto-segmented contours can be used to study cardiac dose-responses in clinical practice.

Project description:This paper studies the feasibility of developing an automatic anatomy segmentation (AAS) system in clinical radiology and demonstrates its operation on clinical 3D images.The AAS system, the authors are developing consists of two main parts: object recognition and object delineation. As for recognition, a hierarchical 3D scale-based multiobject method is used for the multiobject recognition task, which incorporates intensity weighted ball-scale (b-scale) information into the active shape model (ASM). For object delineation, an iterative graph-cut-ASM (IGCASM) algorithm is proposed, which effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. The presented IGCASM algorithm is a 3D generalization of the 2D GC-ASM method that they proposed previously in Chen et al. [Proc. SPIE, 7259, 72590C1-72590C-8 (2009)]. The proposed methods are tested on two datasets comprised of images obtained from 20 patients (10 male and 10 female) of clinical abdominal CT scans, and 11 foot magnetic resonance imaging (MRI) scans. The test is for four organs (liver, left and right kidneys, and spleen) segmentation, five foot bones (calcaneus, tibia, cuboid, talus, and navicular). The recognition and delineation accuracies were evaluated separately. The recognition accuracy was evaluated in terms of translation, rotation, and scale (size) error. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF, FPVF). The efficiency of the delineation method was also evaluated on an Intel Pentium IV PC with a 3.4 GHZ CPU machine.The recognition accuracies in terms of translation, rotation, and scale error over all organs are about 8 mm, 10 degrees and 0.03, and over all foot bones are about 3.5709 mm, 0.35 degrees and 0.025, respectively. The accuracy of delineation over all organs for all subjects as expressed in TPVF and FPVF is 93.01% and 0.22%, and all foot bones for all subjects are 93.75% and 0.28%, respectively. While the delineations for the four organs can be accomplished quite rapidly with average of 78 s, the delineations for the five foot bones can be accomplished with average of 70 s.The experimental results showed the feasibility and efficacy of the proposed automatic anatomy segmentation system: (a) the incorporation of shape priors into the GC framework is feasible in 3D as demonstrated previously for 2D images; (b) our results in 3D confirm the accuracy behavior observed in 2D. The hybrid strategy IGCASM seems to be more robust and accurate than ASM and GC individually; and (c) delineations within body regions and foot bones of clinical importance can be accomplished quite rapidly within 1.5 min.

Project description:BackgroundRetinal and optic disc images are used to assess changes in the retinal vasculature. These can be changes associated with diseases such as diabetic retinopathy and glaucoma or induced using ophthalmodynamometry to measure arterial and venous pressure. Key steps toward automating the assessment of these changes are the segmentation and classification of the veins and arteries. However, such segmentation and classification are still required to be manually labelled by experts. Such automated labelling is challenging because of the complex morphology, anatomical variations, alterations due to disease and scarcity of labelled data for algorithm development. We present a deep machine learning solution called the multiscale guided attention network for retinal artery and vein segmentation and classification (MSGANet-RAV).MethodsMSGANet-RAV was developed and tested on 383 colour clinical optic disc images from LEI-CENTRAL, constructed in-house and 40 colour fundus images from the AV-DRIVE public dataset. The datasets have a mean optic disc occupancy per image of 60.6% and 2.18%, respectively. MSGANet-RAV is a U-shaped encoder-decoder network, where the encoder extracts multiscale features, and the decoder includes a sequence of self-attention modules. The self-attention modules explore, guide and incorporate vessel-specific structural and contextual feature information to segment and classify central optic disc and retinal vessel pixels.ResultsMSGANet-RAV achieved a pixel classification accuracy of 93.15%, sensitivity of 92.19%, and specificity of 94.13% on LEI-CENTRAL, outperforming several reference models. It similarly performed highly on AV-DRIVE with an accuracy, sensitivity and specificity of 95.48%, 93.59% and 97.27%, respectively.ConclusionThe results show the efficacy of MSGANet-RAV for identifying central optic disc and retinal arteries and veins. The method can be used in automated systems designed to assess vascular changes in retinal and optic disc images quantitatively.

Dataset Information

A graph-cut approach for pulmonary artery-vein segmentation in noncontrast CT images.

Publications

A graph-cut approach for pulmonary artery-vein segmentation in noncontrast CT images.

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