Dataset Information

Expected Label Value Computation for Atlas-Based Image Segmentation.

ABSTRACT: The use of multiple atlases is common in medical image segmentation. This typically requires deformable registration of the atlases (or the average atlas) to the new image, which is computationally expensive and susceptible to entrapment in local optima. We propose to instead consider the probability of all possible transformations and compute the expected label value (ELV), thereby not relying merely on the transformation resulting from the registration. Moreover, we do so without actually performing deformable registration, thus avoiding the associated computational costs. We evaluate our ELV computation approach by applying it to liver segmentation on a dataset of computed tomography (CT) images.

SUBMITTER: Aganj I

PROVIDER: S-EPMC6656371 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Expected Label Value Computation for Atlas-Based Image Segmentation.

Aganj Iman I Fischl Bruce B

Proceedings. IEEE International Symposium on Biomedical Imaging 20190401

The use of multiple atlases is common in medical image segmentation. This typically requires deformable registration of the atlases (or the average atlas) to the new image, which is computationally expensive and susceptible to entrapment in local optima. We propose to instead consider the probability of all possible transformations and compute the <i>expected label value (ELV)</i>, thereby not relying merely on the transformation resulting from the registration. Moreover, we do so without actual ...[more]

PMID: 31341547

Similar Datasets

Project description:BackgroundPositron emission tomography (PET)/computed tomography (CT) with [18F]fluorodeoxyglucose {[18F]FDG} has been shown to be an effective imaging method for the lateralization and localization of epilepsy. However, the efficacy of PET/CT image processing and analysis needs to be improved for clinical application. Our previous research proposed a novel atlas-based image method for PET brain image segmentation and quantification; in this study, we evaluated its effectiveness in clinical patients.MethodsFor image segmentation, a head anatomy template was registered to the subject image by integrating dual-modality image registration and landmark-constraint. The localizations of abnormalities were examined by quantitative comparison using the collected database. The PET/CT images of 20 reference patients and 11 patients with epilepsy were used to compare results between the proposed manual method and statistical parameter mapping (SPM). A dice coefficient analysis was performed on the six central brain regions to assess the segmentation effectiveness, and the diagnostic results of the epileptic regions were examined using pathological results as a reference.ResultsThe dice results of the proposed method were generally higher than those of SPM, with the averaged dice values for the proposed method and SPM being 0.78 and 0.55, respectively, in the reference group (P<0.001), and 0.73 and 0.48, respectively, in the epileptic group (P<0.001). Our proposed method detected all the pathologically reported epileptic defects; however, using the visual assessment method, epileptic defects were missed in three patients. Both the proposed and visual assessment methods incorrectly identified non-epileptic areas as epileptic areas.ConclusionsThe results provide strong evidence of the feasibility of using our proposed method for accurate brain region segmentation in the diagnosis of epilepsy. Our atlas-based approach has promise for clinical application in the image processing and diagnosis of patients with epilepsy.

Project description:Cardiac computed tomography angiography (CTA) is an emerging imaging modality for assessing coronary artery as well as various cardiovascular structures. Recently, deep learning (DL) methods have been successfully applied to many applications of medical image analysis including cardiac CTA structure segmentation. However, DL requires a large amounts of data and high-quality labels for training which can be burdensome to obtain due to its labor-intensive nature. In this study, we aim to develop a fully automatic artificial intelligence (AI) system, named DeepHeartCT, for accurate and rapid cardiac CTA segmentation based on DL. The proposed system was trained using a large clinical dataset with computer-generated labels to segment various cardiovascular structures including left and right ventricles (LV, RV), left and right atria (LA, RA), and LV myocardium (LVM). This new system was trained directly using high-quality computer labels generated from our previously developed multi-atlas based AI system. In addition, a reverse ranking strategy was proposed to assess the segmentation quality in the absence of manual reference labels. This strategy allowed the new framework to assemble optimal computer-generated labels from a large dataset for effective training of a deep convolutional neural network (CNN). A large clinical cardiac CTA studies (n = 1,064) were used to train and validate our framework. The trained model was then tested on another independent dataset with manual labels (n = 60). The Dice score, Hausdorff distance and mean surface distance were used to quantify the segmentation accuracy. The proposed DeepHeartCT framework yields a high median Dice score of 0.90 [interquartile range (IQR), 0.90-0.91], a low median Hausdorff distance of 7 mm (IQR, 4-15 mm) and a low mean surface distance of 0.80 mm (IQR, 0.57-1.29 mm) across all segmented structures. An additional experiment was conducted to evaluate the proposed DL-based AI framework trained with a small vs. large dataset. The results show our framework also performed well when trained on a small optimal training dataset (n = 110) with a significantly reduced training time. These results demonstrated that the proposed DeepHeartCT framework provides accurate and rapid cardiac CTA segmentation that can be readily generalized for handling large-scale medical imaging applications.

Dataset Information

Expected Label Value Computation for Atlas-Based Image Segmentation.

Publications

Expected Label Value Computation for Atlas-Based Image Segmentation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets