Dice Similarity Coefficient Metric Research Articles

Clinically oriented automatic three-dimensional enamel segmentation via deep learning

BackgroundEstablishing accurate, reliable, and convenient methods for enamel segmentation and analysis is crucial for effectively planning endodontic, orthodontic, and restorative treatments, as well as exploring the evolutionary patterns of mammals. However, no mature, non-destructive method currently exists in clinical dentistry to quickly, accurately, and comprehensively assess the integrity and thickness of enamel chair-side. This study aims to develop a deep learning work, 2.5D Attention U-Net, trained on small sample datasets, for the automatical, efficient, and accurate segmentation of enamel across all teeth in clinical settings.MethodsWe propose a fully automated computer-aided enamel segmentation model based on an instance segmentation network, 2.5D Attention U-Net. After data annotation and augmentation, the model is trained using manually annotated segmented enamel data, and its performance is evaluated using the Dice similarity coefficient metrics. A satisfactory image segmentation model is applied to generate a 3D enamel model for each tooth and to calculate the thickness value of individual enclosed 3D enamel meshes using a normal ray-tracing directional method.ResultsThe model achieves the Dice score on the enamel segmentation task of 96.6%. This study provides an intuitive visualization of irregular enamel morphology and a quantitative analysis of three-dimensional enamel thickness variations. The results indicate that enamel is thickest at the incisal edges of anterior teeth and the cusps of posterior teeth, thinning towards the roots. For posterior teeth, the enamel is thinnest at the central fossae area, with mandibular molars having thicker enamel in the central fossae compared to maxillary molars. The average enamel thickness of maxillary incisors, canines, and premolars is greater than that of mandibular incisors, while the opposite is true for molars. Although there are individual variations in enamel thickness, the average enamel thickness graduallly increases from the incisors to the molars among all teeth within the same quadrant.ConclusionsThis study introduces an automatic, efficient, and accurate 2.5D Attention U-Net system to enhance precise and efficient chair-side diagnosis and treatment of enamel-related diseases in clinical settings, marking a significant advancement in automated diagnostics for enamel-related conditions.

DSKCA-UNet: Dynamic selective kernel channel attention for medical image segmentation.

U-Net has attained immense popularity owing to its performance in medical image segmentation. However, it cannot be modeled explicitly over remote dependencies. By contrast, the transformer can effectively capture remote dependencies by leveraging the self-attention (SA) of the encoder. Although SA, an important characteristic of the transformer, can find correlations between them based on the original data, secondary computational complexity might retard the processing rate of high-dimensional data (such as medical images). Furthermore, SA is limited because the correlation between samples is overlooked; thus, there is considerable scope for improvement. To this end, based on Swin-UNet, we introduce a dynamic selective attention mechanism for the convolution kernels. The weight of each convolution kernel is calculated to fuse the results dynamically. This attention mechanism permits each neuron to adaptively modify its receptive field size in response to multiscale input information. A local cross-channel interaction strategy without dimensionality reduction was introduced, which effectively eliminated the influence of downscaling on learning channel attention. Through suitable cross-channel interactions, model complexity can be significantly reduced while maintaining its performance. Subsequently, the global interaction between the encoder features is used to extract more fine-grained features. Simultaneously, the mixed loss function of the weighted cross-entropy loss and Dice loss is used to alleviate category imbalances and achieve better results when the sample number is unbalanced. We evaluated our proposed method on abdominal multiorgan segmentation and cardiac segmentation datasets, achieving Dice similarity coefficient and 95% Hausdorff distance metrics of 80.30 and 14.55%, respectively, on the Synapse dataset and Dice similarity coefficient metrics of 90.80 on the ACDC dataset. The experimental results show that our proposed method has good generalization ability and robustness, and it is a powerful tool for medical image segmentation.

Clinical evaluation on automatic segmentation results of convolutional neural networks in rectal cancer radiotherapy.

Image segmentation can be time-consuming and lacks consistency between different oncologists, which is essential in conformal radiotherapy techniques. We aimed to evaluate automatic delineation results generated by convolutional neural networks (CNNs) from geometry and dosimetry perspectives and explore the reliability of these segmentation tools in rectal cancer. Forty-seven rectal cancer cases treated from February 2018 to April 2019 were randomly collected retrospectively in our cancer center. The oncologists delineated regions of interest (ROIs) on planning CT images as the ground truth, including clinical target volume (CTV), bladder, small intestine, and femoral heads. The corresponding automatic segmentation results were generated by DeepLabv3+ and ResUNet, and we also used Atlas-Based Autosegmentation (ABAS) software for comparison. The geometry evaluation was carried out using the volumetric Dice similarity coefficient (DSC) and surface DSC, and critical dose parameters were assessed based on replanning optimized by clinically approved or automatically generated CTVs and organs at risk (OARs), i.e., the Planref and Plantest. Pearson test was used to explore the correlation between geometric metrics and dose parameters. In geometric evaluation, DeepLabv3+ performed better in DCS metrics for the CTV (volumetric DSC, mean = 0.96, P< 0.01; surface DSC, mean = 0.78, P< 0.01) and small intestine (volumetric DSC, mean = 0.91, P< 0.01; surface DSC, mean = 0.62, P< 0.01), ResUNet had advantages in volumetric DSC of the bladder (mean = 0.97, P< 0.05). For critical dose parameters analysis between Planref and Plantest, there was a significant difference for target volumes (P< 0.01), and no significant difference was found for the ResUNet-generated small intestine (P > 0.05). For the correlation test, a negative correlation was found between DSC metrics (volumetric, surface DSC) and dosimetric parameters (δD95, δD95, HI, CI) for target volumes (P< 0.05), and no significant correlation was found for most tests of OARs (P > 0.05). CNNs show remarkable repeatability and time-saving in automatic segmentation, and their accuracy also has a certain potential in clinical practice. Meanwhile, clinical aspects, such as dose distribution, may need to be considered when comparing the performance of auto-segmentation methods.

Feature interaction network based on hierarchical decoupled convolution for 3D medical image segmentation.

Manual image segmentation consumes time. An automatic and accurate method to segment multimodal brain tumors using context information rich three-dimensional medical images that can be used for clinical treatment decisions and surgical planning is required. However, it is a challenge to use deep learning to achieve accurate segmentation of medical images due to the diversity of tumors and the complex boundary interactions between sub-regions while limited computing resources hinder the construction of efficient neural networks. We propose a feature fusion module based on a hierarchical decoupling convolution network and an attention mechanism to improve the performance of network segmentation. We replaced the skip connections of U-shaped networks with a feature fusion module to solve the category imbalance problem, thus contributing to the segmentation of more complicated medical images. We introduced a global attention mechanism to further integrate the features learned by the encoder and explore the context information. The proposed method was evaluated for enhance tumor, whole tumor, and tumor core, achieving Dice similarity coefficient metrics of 0.775, 0.900, and 0.827, respectively, on the BraTS 2019 dataset and 0.800, 0.902, and 0.841, respectively on the BraTS 2018 dataset. The results show that our proposed method is inherently general and is a powerful tool for brain tumor image studies. Our code is available at: https://github.com/WSake/Feature-interaction-network-based-on-Hierarchical-Decoupled-Convolution.

MLRD-Net: 3D multiscale local cross-channel residual denoising network for MRI-based brain tumor segmentation

The precise segmentation of multimodal MRI images is the primary stage of tumor diagnosis and treatment. Current segmentation strategies often underutilize multiscale features, which can easily lead to loss of contextual information, reduction of low-level features and noise interference. To overcome these issues, a 3D multiscale local cross-channel residual denoising network (MLRD-Net) for an MRI-based brain tumor segmentation algorithm is proposed in this paper. Specifically, we employ encoder-decoder structure to connect local and global features, and enhance the receptive field of the network. Random slice operation has been conducted to enhance robustness. Then, residual blocks with pre-activation operation are developed in down-sampling stage, which effectively improves signal propagation along the network and alleviates network overfitting. Finally, the local cross-channel denoising mechanism is established to eliminate unimportant features without dimensionality reduction. Our proposal was evaluated in Brain Tumor Segmentation 2020 dataset (BraTS 2020), obtaining significantly improved results with mean Dice Similarity Coefficient metric of 0.91, 0.79, and 0.73 for the complete, tumor core, and enhancing tumor regions respectively. Besides, we conduct further practice on BraTS 2019, with the mean Dice Similarity Coefficient metric of 0.89, 0.80, and 0.75. Massive experiments demonstrate that our method is powerful and reliable. It increases little model complexity while achieving very competitive performance.

Automatic Segmentation of Lumbar Spine MRI Images Based on Improved Attention U-Net.

Lumbar spine segmentation is important to help doctors diagnose lumbar disc herniation (LDH) and patients' rehabilitation treatment. In order to accurately segment the lumbar spine, a lumbar spine image segmentation algorithm based on improved Attention U-Net is proposed. The algorithm is based on Attention U-Net, the attention module based on multilevel feature map fusion is adopted, two residual modules are introduced instead of the original convolution blocks. a hybrid loss function is used for prediction during the training process, and finally, the image superposition process is realized. In this experiment, we expanded 420 lumbar MRI images of 180 patients to 1000 images and trained them by different algorithms, respectively, and accuracy, recall, and Dice similarity coefficient metrics were used to analyze these algorithms. The results show that compared with SVM, FCN, R-CNN, U-Net, and Attention U-Net models, the improved model achieved better results in all three evaluations, with 95.50%, 94.53%, and 95.01%, respectively, which proves the better performance of the proposed method for segmentation in lumbar disc and caudal vertebrae.

Graph deep network for optic disc and optic cup segmentation for glaucoma disease using retinal imaging.

The fundus imaging method of eye screening detects eye diseases by segmenting the optic disc (OD) and optic cup (OC). OD and OC are still challenging to segment accurately. This work proposes three-layer graph-based deep architecture with an enhanced fusion method for OD and OC segmentation. CNN encoder-decoder architecture, extended graph network, and approximation via fusion-based rule are explored for connecting local and global information. A graph-based model is developed for combining local and overall knowledge. By extending feature masking, regularization of repetitive features with fusion for combining channels has been done. The performance of the proposed network is evaluated through the analysis of different metric parameters such as dice similarity coefficient (DSC), intersection of union (IOU), accuracy, specificity, sensitivity. Experimental verification of this methodology has been done using the four benchmarks publicly available datasets DRISHTI-GS, RIM-ONE for OD, and OC segmentation. In addition, DRIONS-DB and HRF fundus imaging datasets were analyzed for optimizing the model's performance based on OD segmentation. DSC metric of methodology achieved 0.97 and 0.96 for DRISHTI-GS and RIM-ONE, respectively. Similarly, IOU measures for DRISHTI-GS and RIM-ONE datasets were 0.96 and 0.93, respectively, for OD measurement. For OC segmentation, DSC and IOU were measured as 0.93 and 0.90 respectively for DRISHTI-GS and 0.83 and 0.82 for RIM-ONE data. The proposed technique improved value of metrics with most of the existing methods in terms of DSC and IOU of the results metric of the experiments for OD and OC segmentation.

Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements

BackgroundRight atrial (RA) area predicts mortality in patients with pulmonary hypertension, and is recommended by the European Society of Cardiology/European Respiratory Society pulmonary hypertension guidelines. The advent of deep learning may allow more reliable measurement of RA areas to improve clinical assessments. The aim of this study was to automate cardiovascular magnetic resonance (CMR) RA area measurements and evaluate the clinical utility by assessing repeatability, correlation with invasive haemodynamics and prognostic value.MethodsA deep learning RA area CMR contouring model was trained in a multicentre cohort of 365 patients with pulmonary hypertension, left ventricular pathology and healthy subjects. Inter-study repeatability (intraclass correlation coefficient (ICC)) and agreement of contours (DICE similarity coefficient (DSC)) were assessed in a prospective cohort (n = 36). Clinical testing and mortality prediction was performed in n = 400 patients that were not used in the training nor prospective cohort, and the correlation of automatic and manual RA measurements with invasive haemodynamics assessed in n = 212/400. Radiologist quality control (QC) was performed in the ASPIRE registry, n = 3795 patients. The primary QC observer evaluated all the segmentations and recorded them as satisfactory, suboptimal or failure. A second QC observer analysed a random subcohort to assess QC agreement (n = 1018).ResultsAll deep learning RA measurements showed higher interstudy repeatability (ICC 0.91 to 0.95) compared to manual RA measurements (1st observer ICC 0.82 to 0.88, 2nd observer ICC 0.88 to 0.91). DSC showed high agreement comparing automatic artificial intelligence and manual CMR readers. Maximal RA area mean and standard deviation (SD) DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 is 92.4 ± 3.5 cm2, 91.2 ± 4.5 cm2 and 93.2 ± 3.2 cm2, respectively. Minimal RA area mean and SD DSC metric for observer 1 vs observer 2, automatic measurements vs observer 1 and automatic measurements vs observer 2 was 89.8 ± 3.9 cm2, 87.0 ± 5.8 cm2 and 91.8 ± 4.8 cm2. Automatic RA area measurements all showed moderate correlation with invasive parameters (r = 0.45 to 0.66), manual (r = 0.36 to 0.57). Maximal RA area could accurately predict elevated mean RA pressure low and high-risk thresholds (area under the receiver operating characteristic curve artificial intelligence = 0.82/0.87 vs manual = 0.78/0.83), and predicted mortality similar to manual measurements, both p < 0.01. In the QC evaluation, artificial intelligence segmentations were suboptimal at 108/3795 and a low failure rate of 16/3795. In a subcohort (n = 1018), agreement by two QC observers was excellent, kappa 0.84.ConclusionAutomatic artificial intelligence CMR derived RA size and function are accurate, have excellent repeatability, moderate associations with invasive haemodynamics and predict mortality.

Variability of gross tumour volume delineation: MRI and CT based tumour and lymph node delineation for lung radiotherapy

PurposeTo compare gross tumour volume (GTV) delineation of lung cancer on magnetic resonance imaging (MRI) and positron emission tomography (PET) versus computed tomography (CT) and PET. MethodsThree experienced thoracic radiation oncologists delineated GTVs on twenty-six patients with lung cancer, based on CT registered to PET, T2-weighted MRI registered to PET and T1-weighted MRI registered with PET. All observers underwent education on reviewing T1 and T2 images along with guidance on window and level setup. Interobserver and intermodality variation was performed based on dice similarity coefficient (DSC), Hausdorff distance (HD), and average Hausdorff distance (AvgHD) metrics. To compute interobserver variability (IOV) a simultaneous truth and performance level estimation (STAPLE) volume for each image modality was used as reference volume. For intermodality analysis, each observers CT based primary and nodal GTV was used as reference volume. ResultsA mean DSC of 0.9 across all observers for primary GTV (GTVp) and a DSC of >0.7 for nodal GTV (GTVn) was demonstrated for IOV. Mean T2 and T1 GTVp and GTVn were smaller than CT GTVp and GTVn but the difference in volume between modalities was not statistically significant. Significant difference (p < 0.01) for GTVp and GTVn was found between T2 and T1 GTVp and GTVn compared to CT GTVp and GTVn based on DSC metrics. Large variation in volume similarity was noted based on HD of up-to 5.4 cm for observer volumes compared to STAPLE volume. ConclusionInterobserver variability in GTV delineation was similar for MRI and PET versus CT and PET. The significant difference between MRI compared to CT delineated volumes needs to be further explored.

Lung Cancer Segmentation With Transfer Learning: Usefulness of a Pretrained Model Constructed From an Artificial Dataset Generated Using a Generative Adversarial Network.

Purpose: The purpose of this study was to develop and evaluate lung cancer segmentation with a pretrained model and transfer learning. The pretrained model was constructed from an artificial dataset generated using a generative adversarial network (GAN). Materials and Methods: Three public datasets containing images of lung nodules/lung cancers were used: LUNA16 dataset, Decathlon lung dataset, and NSCLC radiogenomics. The LUNA16 dataset was used to generate an artificial dataset for lung cancer segmentation with the help of the GAN and 3D graph cut. Pretrained models were then constructed from the artificial dataset. Subsequently, the main segmentation model was constructed from the pretrained models and the Decathlon lung dataset. Finally, the NSCLC radiogenomics dataset was used to evaluate the main segmentation model. The Dice similarity coefficient (DSC) was used as a metric to evaluate the segmentation performance. Results: The mean DSC for the NSCLC radiogenomics dataset improved overall when using the pretrained models. At maximum, the mean DSC was 0.09 higher with the pretrained model than that without it. Conclusion: The proposed method comprising an artificial dataset and a pretrained model can improve lung cancer segmentation as confirmed in terms of the DSC metric. Moreover, the construction of the artificial dataset for the segmentation using the GAN and 3D graph cut was found to be feasible.

Segmentation of the multimodal brain tumor image used the multi-pathway architecture method based on 3D FCN

Segmentation of multimodal brain tissues from 3D medical images is of great significance for brain diagnosis. It is required to create an automated and accurate segmentation based on deep-learning network due to the manual segmentation is time-consuming. However, how to segment medical images accurately and how to build neural network effectively with very limited computing resource, is still challenging task. To address these problems, we propose a novel model based on 3D fully convolutional network. More specifically, we apply multi-pathway architecture to feature extraction so as to effectively extract features from multi-modal MRI images. Different receptive field of feature have been extracted by adopting 3D dilated convolution in each pathway. By evaluating one-pathway model and key components of our model with a set of effective training schemes, we analyzed how these alternative methods affect the performance of experiments. Our proposed model was evaluated in the Brain Tumor Segmentation 2019 dataset (BraTS 2019), making an effective segmentation for the complete, core and enhancing tumor regions in Dice Similarity Coefficient metric (0.89, 0.78, 0.76) for the dataset. Also, we made a practice on BraTS 2018 using the same method with the Dice Similarity Coefficient metric of 0.90, 0.79, and 0.77 for the complete, core and enhancing tumor regions. Our method is inherently general and is a powerful tool to studies of medical images of brain tumors. Our code is available at https://github.com/JalexDooo/BrainstormTS.

Liver Segmentation in Computed Tomography Abdomen Images Based on Particle Swarm Optimization and Morphology

Segmenting liver from abdominal images is a thought-provoking task. A method for segmenting liver region from CT abdomen is proposed in this paper. Particle Swarm Optimization (PSO) method is employed for segmenting multiple regions in the abdomen image. Morphological operation such as Erosion and Dilation is used for segmenting exact portion of liver. Largest connected component and filling holes operation are applied as supporting techniques for image corrections. Experiment on our proposed segmentation approach is carried out and the results are discussed. The quantitative validation was performed with Dice similarity co-efficient metric.

Brain tumor segmentation with deep convolutional symmetric neural network

Gliomas are the most frequent primary brain tumors, which have a high mortality. Surgery is the most commonly used treatment. Magnetic resonance imaging (MRI) is especially useful to assess gliomas and evaluate the success of the treatment in clinical practice. So accurately segmenting the brain tumor from MRI images is the key to clinical diagnostics and treatment planning. However, a large quantity of data produced by MRI prevents manual segmentation in a reasonable time. So automatic approaches are required for quick and effective segmentation. But the spatial and structural variability among brain tumors bring a challenge to automatically segment MRI images. Deep Convolutional Neural Network (DCNN) can achieve complex function mapping. This contributes to response to the challenge. Therefore, DCNN is the commonly used method in brain tumor segmentation. Although a large number of DCNN based methods have been proposed, these methods mainly aim to improve the quality of the image features extracted by DCNN. Actually, these methods usually ignore the prior knowledge in medical images, and the simple prior knowledge in brain tumor segmentation is that most of the tumor regions in images are left-right asymmetry. Based on this, in this paper, a novel deep convolutional neural network which combines symmetry have been proposed to automatically segment brain tumors. Our neural networks, called Deep Convolutional Symmetric Neural Network (DCSNN), extends DCNN based segmentation networks by adding symmetric masks in several layers. Our proposal was validated in the BRATS 2015 database, and we also give some baselines. The results are evaluated by dice similarity coefficient metric (DSC). And our proposed method achieved a competitive result with average DSC of 0.852. Furthermore, our method only takes about 10.8 s to segment a patient case. Although our method is not the best performance in BRATS 2015 challenge, as far as we know, our method outperforms other recent DCNN-based methods.

Estimating the projected frontal surface area of cyclists from images using a variational framework and statistical shape and appearance models

We present a computer vision-based approach to estimating the projected frontal surface area (pFSA) of cyclists from unconstrained images. Wind tunnel studies show a reduction in cyclists’ aerodynamic drag through manipulation of the cyclist’s pose. Whilst the mechanism by which reduction is achieved remains unknown, it is widely accepted in the literature that the drag is proportional to the cyclist’s pFSA. This paper describes a repeatable automatic method for pFSA estimation for the study of its relationship with aerodynamic drag in cyclists. The proposed approach is based on finding object boundaries in images. An initialised curve dynamically evolves in the image to minimise an energy function designed to force the curve to gravitate towards image features. To overcome occlusions and pose variation, we use a statistical cyclist shape and appearance models as priors to encourage the evolving curve to arrive at the desired solution. Contour initialisation is achieved using a discriminative object detection method based on offline supervised learning that yields a cyclist classifier. Once an instance of a cyclist is detected in an image and segmented, the pFSA is calculated from the area of the final curve. Applied to two challenging datasets of cyclist images, for cyclist detection our method achieves precision scores of 1.0 and 0.96 and recall scores of 0.68 and 0.83 on the wind tunnel and cyclists-in-natura datasets, respectively. For cyclist segmentation, it achieves 0.88 and 0.92 scores for the mean dice similarity coefficient metric on the two datasets, respectively. We discuss the performance of our method under occlusion, orientation, and pose conditions. Our method successfully estimates pFSA of cyclists and opens new vistas for exploration of the relationship between pFSA and aerodynamic drag.

Comparison of Three Segmentation Methods for Breast Ultrasound Images based on Level Set and Morphological Operations

Breast cancer is one of the major causes of death among women all over the world. The most frequently used diagnosis tool to detect breast cancer is ultrasound. However, to segment the breast ultrasound images is a difficult thing. Some studies show that the active contour models have been proved to be the most successful methods for medical image segmentation. The level set method is a class of curve evolution methods based on the geometric active contour model. Morphological operation describes a range of image processing technique that deal with the shape of features in an image. Morphological operations are applied to remove imperfections that introduced during segmentation. In this paper, we have evaluated three level set methods that combined with morphological operations to segment the breast lesions. The level set methods that used in our research are the Chan Vese (C-V) model, the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) model and the Distance Regularized Level Set Evolution (DRLSE) model. Furthermore, to evaluate the method, we compared the segmented breast lesion that obtained by each method with the lesion that obtained manually by radiologists. The evaluation is done by four metrics: Dice Similarity Coefficient (DSC), True-Positive Ratio (TPR), True-Negative Ratio (TNR), and Accuracy (ACC). Our experimental results with 30 breast ultrasound images showed that the C-V model that combined with morphological operations have better performance than the other two methods according to mean value of DSC metrics.

Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.

Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 ×3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0.88, 0.83, 0.77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0.78, 0.65, and 0.75 for the complete, core, and enhancing regions, respectively.

Influence of filter choice on 18F-FDG PET segmentation accuracy determined using generalized estimating equations

This study aims to quantify how filter choice affects several fluoro-deoxy-glucose (FDG)-positron emission tomography (PET) segmentation methods and present the use of model fitting via generalized estimating equations (GEEs) to appropriately account for the properties of a common segmentation quality metric (Dice similarity coefficient). Spherical and irregularly shaped ‘hot’ objects filled with 18F-FDG were placed in a medium with background activity and imaged for 1, 2 and 5 min durations at low and high contrasts. Images were filtered with Gaussian and bilateral filters of 5 and 7 mm full-width half maximum (FWHM), with and without 3 mm FWHM Gaussian pre-smoothing. Four segmentation methods were used: 40% thresholding, adaptive thresholding, k-means clustering and seeded region-growing. Segmentation accuracy was quantified by overlap (using Dice similarity coefficient (DSC)) and distance between surfaces (using symmetric-mean-absolute-surface-distance (SMASD)) of the ground truth and segmented volumes. All segmentation methods showed mean DSC values between 0.71–0.87 and mean SMASD values between 0.72–2.10 mm across filters. The bilateral filter with 3 mm FWHM Gaussian pre-smoothing had mean DSC 0.80 ± 0.17 and mean SMASD 1.17 ± 1.51 mm displaying approximately equal performance to a 5 mm Gaussian filter with mean DSC 0.79 ± 0.18 and mean SMASD 1.27 ± 1.52 mm. Results from models fit using GEE with a binomial distribution and exchangeable correlation structure estimated the correlation between DSC values as 0.118 and 0.290 for spheres and irregular objects, respectively. The GEE approach accounts for several factors specific to the DSC metric that simpler statistical approaches do not, providing more accurate estimations of experimental effects commonly associated with nuclear medicine segmentation studies.