Ground-truth Results Research Articles

Reproducibility of arterial spin labeling cerebral blood flow image processing: A report of the ISMRM open science initiative for perfusion imaging (OSIPI) and the ASL MRI challenge.

Arterial spin labeling (ASL) is a widely used contrast-free MRI method for assessing cerebral blood flow (CBF). Despite the generally adopted ASL acquisition guidelines, there is still wide variability in ASL analysis. We explored this variability through the ISMRM-OSIPI ASL-MRI Challenge, aiming to establish best practices for more reproducible ASL analysis. Eight teams analyzed the challenge data, which included a high-resolution T1-weighted anatomical image and 10 pseudo-continuous ASL datasets simulated using a digital reference object to generate ground-truth CBF values in normal and pathological states. We compared the accuracy of CBF quantification from each team's analysis to the ground truth across all voxels and within predefined brain regions. Reproducibility of CBF across analysis pipelines was assessed using the intra-class correlation coefficient (ICC), limits of agreement (LOA), and replicability of generating similar CBF estimates from different processing approaches. Absolute errors in CBF estimates compared to ground-truth synthetic data ranged from 18.36 to 48.12 mL/100 g/min. Realistic motion incorporated into three datasets produced the largest absolute error and variability between teams, with the least agreement (ICC and LOA) with ground-truth results. Fifty percent of the submissions were replicated, and one produced three times larger CBF errors (46.59 mL/100 g/min) compared to submitted results. Variability in CBF measurements, influenced by differences in image processing, especially to compensate for motion, highlights the significance of standardizing ASL analysis workflows. We provide a recommendation for ASL processing based on top-performing approaches as a step toward ASL standardization.

Propagation of uncertainty for an epipole-dependent model for convergent stereovision structure computation

An analytic model incorporating stereo epipoles is proposed for structure computation using a convergent stereovision setup. The developed model is predicated on the image parameters of both CCD camera sensors, together with two extrinsic parameters, namely the stereo baseline distance and the stereo projection angle of the scene point of interest. In the model, the points on the image planes are measured relative to the principal points, stereo epipoles are featured, and only focal length-normalized camera sensor coordinates are required for structure computation. The reconstruction model could be employed in active vision-based metrology in which the stereo imaging cameras are systematically rotated about their vertical axes relative to each other. The performance of the model is studied, and its accuracy tested by comparing the 3-space coordinates it predicted to the those obtained by a gold standard triangulation and to the ground truth results. In terms of execution speed the proposed reconstruction model exhibited a computation time of 0.6 ms compared to 6.2 ms and 9.9 ms recorded for the direct linear transformation and gold standard triangulation algorithms respectively. The coordinate measurement uncertainties determined by experimental methods are subsequently compared with those obtained by a theoretical approach based on the analytic reconstruction model. Strong correlations were found to exist between the two sets of uncertainty values obtained.

MixTURE: L1-Norm-Based Mixed Second-Order Continuity in Strain Tensor Ultrasound Elastography.

Energy-based displacement tracking of ultrasound images can be implemented by optimizing a cost function consisting of a data term, a mechanical congruency term, and first- and second-order continuity terms. This approach recently provided a promising solution to two-dimensional axial and lateral displacement tracking in ultrasound strain elastography. However, the associated second-order regularizer only considers the unmixed second derivatives and disregards the mixed derivatives, thereby providing suboptimal noise suppression and limiting possibilities for total strain tensor imaging. We propose to improve axial, lateral, axial shear, and lateral shear strain estimation quality by formulating and optimizing a novel L1-norm-based second-order regularizer that penalizes both mixed and unmixed displacement derivatives. We name the proposed technique L1-MixTURE, which stands for L1-norm Mixed derivative for Total UltRasound Elastography. When compared to simulated ground truth results, the mean structural similarity (MSSIM) obtained with L1-MixTURE ranged 0.53 to 0.86 and the mean absolute error (MAE) ranged 0.00053 to 0.005. In addition, the mean elastographic signal-to-noise ratio (SNR) achieved with simulated, experimental phantom, and in vivo breast datasets, ranged 1.87 to 52.98, and the mean elastographic contrast-to-noise ratio (CNR) ranged 7.40 to 24.53. When compared to a closely related existing technique that does not consider the mixed derivatives, L1-MixTURE generally outperformed the MSSIM, MAE, SNR, and CNR by up to 37.96%, 67.82%, and 25.53% in the simulated, experimental phantom, and in vivo datasets, respectively. These results collectively highlight the ability of L1-MixTURE to deliver highly accurate axial, lateral, axial shear, and lateral shear strain estimates and advance the state-of-the-art in elastography-guided diagnostic and interventional decisions.

A Novel Contactless Blood Pressure Measurement System and Algorithm Based on Vision Intelligence

The measurement of vital signs such as blood pressure plays a key role in human health. Usually, we encounter some problems when we check them in the traditional way; for example, it is impossible to check continuously, and measuring vital signs requires direct contact with the patient, which can be uncomfortable for certain individuals. In this research, we present a vision-based system for estimating blood pressure using pulse transit time (PTT) and the Eulerian video magnification (EVM) technique to amplify tiny color variations caused by blood flow to calculate arterial pulse waves traveling between two arterial sites. Calculating the PTT by processing the video signal for each subject, an oscillometer BP device was used to evaluate the performance between measurements in different conditions, including rest, exercise, and during recovery. Mean systolic BP was 115 mmHg at rest, 137 mmHg during high-intensity exercise, and 114 mmHg during recovery, respectively. The average value of diastolic blood pressure did not change significantly before, during, and after exercise. When we compared the systolic and diastolic blood pressure with ground-truth results, our system showed an accuracy of 91% for systolic blood pressure and 90% for diastolic blood pressure.

Rapid Quantification of Microvessels of Three-Dimensional Blood-Brain Barrier Model Using Optical Coherence Tomography and Deep Learning Algorithm.

The blood-brain barrier (BBB) is a selective barrier that controls the transport between the blood and neural tissue features and maintains brain homeostasis to protect the central nervous system (CNS). In vitro models can be useful to understand the role of the BBB in disease and assess the effects of drug delivery. Recently, we reported a 3D BBB model with perfusable microvasculature in a Transwell insert. It replicates several key features of the native BBB, as it showed size-selective permeability of different molecular weights of dextran, activity of the P-glycoprotein efflux pump, and functionality of receptor-mediated transcytosis (RMT), which is the most investigated pathway for the transportation of macromolecules through endothelial cells of the BBB. For quality control and permeability evaluation in commercial use, visualization and quantification of the 3D vascular lumen structures is absolutely crucial. Here, for the first time, we report a rapid, non-invasive optical coherence tomography (OCT)-based approach to quantify the microvessel network in the 3D in vitro BBB model. Briefly, we successfully obtained the 3D OCT images of the BBB model and further processed the images using three strategies: morphological imaging processing (MIP), random forest machine learning using the Trainable Weka Segmentation plugin (RF-TWS), and deep learning using pix2pix cGAN. The performance of these methods was evaluated by comparing their output images with manually selected ground truth images. It suggested that deep learning performed well on object identification of OCT images and its computation results of vessel counts and surface areas were close to the ground truth results. This study not only facilitates the permeability evaluation of the BBB model but also offers a rapid, non-invasive observational and quantitative approach for the increasing number of other 3D in vitro models.

A Demonstration of DLBD: Database Logic Bug Detection System

Database management systems (DBMSs) are prone to logic bugs that can result in incorrect query results. Current debugging tools are limited to single table queries and struggle with issues like lack of ground-truth results and repetitive query space exploration. In this paper, we demonstrate DLBD, a system that automatically detects logic bugs in databases. DLBD offers holistic logic bug detection by providing automatic schema and query generation and ground-truth query result retrieval. Additionally, DLBD provides minimal test cases and root cause analysis for each bug to aid developers in reproducing and fixing detected bugs. DLBD incorporates heuristics and domain-specific knowledge to efficiently prune the search space and employs query space exploration mechanisms to avoid the repetitive search. Finally, DLBD utilizes a distributed processing framework to test database logic bugs in a scalable and efficient manner. Our system offers developers a reliable and effective way to detect and fix logic bugs in DBMSs.

Deep Learning-Based Digitally Reconstructed Tomography of the Chest in the Evaluation of Solitary Pulmonary Nodules: A Feasibility Study.

Computed tomography (CT) is preferred for evaluating solitary pulmonary nodules (SPNs) but access or availability may be lacking, in addition, overlapping anatomy can hinder detection of SPNs on chest radiographs. We developed and evaluated the clinical feasibility of a deep learning algorithm to generate digitally reconstructed tomography (DRT) images of the chest from digitally reconstructed frontal and lateral radiographs (DRRs) and use them to detect SPNs. This single-institution retrospective study included 637 patients with noncontrast helical CT of the chest (mean age 68 years, median age 69 years, standard deviation 11.7 years; 355 women) between 11/2012 and 12/2020, with SPNs measuring 10-30 mm. A deep learning model was trained on 562 patients, validated on 60 patients, and tested on the remaining 15 patients. Diagnostic performance (SPN detection) from planar radiography (DRRs and CT scanograms, PR) alone or with DRT was evaluated by two radiologists in an independent blinded fashion. The quality of the DRT SPN image in terms of nodule size and location, morphology, and opacity was also evaluated, and compared to the ground-truth CT images RESULTS: Diagnostic performance was higher from DRT plus PR than from PR alone (area under the receiver operating characteristic curve 0.95-0.98 versus 0.80-0.85; p < 0.05). DRT plus PR enabled diagnosis of SPNs in 11 more patients than PR alone. Interobserver agreement was 0.82 for DRT plus PR and 0.89 for PR alone; and interobserver agreement for size and location, morphology, and opacity of the DRT SPN was 0.94, 0.68, and 0.38, respectively. For SPN detection, DRT plus PR showed better diagnostic performance than PR alone. Deep learning can be used to generate DRT images and improve detection of SPNs.

Face Completion Based on Symmetry Awareness with Conditional GAN

Face completion is an important topic in the field of computer vision and image processing. Its core task is to restore image information, so that the generated completion results are as consistent as possible with the ground-truth results. In existing methods, there is no strong constraint on the consistency between the completion result and the true value, and the symmetry characteristics of the face are ignored, which makes it impossible to generate a natural and consistent completion result for any position and symmetrical position of the face. In response to these problems, we propose a novel method called face completion generative adversarial network (FC GAN). Our generator uses a u-net-like structure, and the discriminator uses a combination of global discriminator and local discriminator. We use a new perceptual loss based on VGG-19 to constrain the consistency of the completion result and the true value. We use symmetry awareness in our method and it takes full advantage of face symmetry features to optimize face completion. For irregular mask image completion, our method produces visually realistic and semantically correct results. We evaluate our model on the CelebA dataset and use FID and SSIM as the indicators. Compared with existing methods, the face completion method in this paper has a certain improvement in visual effects and evaluation indicators.

DiVA: A Scalable, Interactive and Customizable Visual Analytics Platform for Information Diffusion on Large Networks

With an increasing outreach of digital platforms in our lives, researchers have taken a keen interest in studying different facets of social interactions. Analyzing the spread of information ( aka diffusion) has brought forth multiple research areas such as modelling user engagement, determining emerging topics, forecasting the virality of online posts and predicting information cascades. Despite such ever-increasing interest, there remains a vacuum among easy-to-use interfaces for large-scale visualization of diffusion models. In this article, we introduce DiVA — Di ffusion V isualization and A nalysis, a tool that provides a scalable web interface and extendable APIs to analyze various diffusion trends on networks. DiVA uniquely offers support for simultaneous comparison of two competing diffusion models and even the comparison with the ground-truth results, which help develop a coherent understanding of real-world scenarios. Along with performing an exhaustive feature comparison and system evaluation of DiVA against publicly-available web interfaces for information diffusion, we conducted a user study to understand the strengths and limitations of DiVA . We noticed that evaluators had a seamless user experience, especially when analyzing diffusion on large networks.

Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application.

PurposeTo develop a method of biologically guided deep learning for post-radiation 18FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information.MethodsBased on the classic reaction–diffusion mechanism, a novel biological model was proposed using a partial differential equation that incorporates spatial radiation dose distribution as a patient-specific treatment information variable. A 7-layer encoder–decoder-based convolutional neural network (CNN) was designed and trained to learn the proposed biological model. As such, the model could generate post-radiation 18FDG-PET image outcome predictions with breakdown biological components for enhanced explainability. The proposed method was developed using 64 oropharyngeal patients with paired 18FDG-PET studies before and after 20-Gy delivery (2 Gy/day fraction) by intensity-modulated radiotherapy (IMRT). In a two-branch deep learning execution, the proposed CNN learns specific terms in the biological model from paired 18FDG-PET images and spatial dose distribution in one branch, and the biological model generates post-20-Gy 18FDG-PET image prediction in the other branch. As in 2D execution, 718/233/230 axial slices from 38/13/13 patients were used for training/validation/independent test. The prediction image results in test cases were compared with the ground-truth results quantitatively.ResultsThe proposed method successfully generated post-20-Gy 18FDG-PET image outcome prediction with breakdown illustrations of biological model components. Standardized uptake value (SUV) mean values in 18FDG high-uptake regions of predicted images (2.45 ± 0.25) were similar to ground-truth results (2.51 ± 0.33). In 2D-based Gamma analysis, the median/mean Gamma Index (<1) passing rate of test images was 96.5%/92.8% using the 5%/5 mm criterion; such result was improved to 99.9%/99.6% when 10%/10 mm was adopted.ConclusionThe developed biologically guided deep learning method achieved post-20-Gy 18FDG-PET image outcome predictions in good agreement with ground-truth results. With the breakdown biological modeling components, the outcome image predictions could be used in adaptive radiotherapy decision-making to optimize personalized plans for the best outcome in the future.

Automatic Detection of Atherosclerotic Plaque and Calcification From Intravascular Ultrasound Images by Using Deep Convolutional Neural Networks.

Atherosclerosis is the major cause of cardiovascular diseases (CVDs). Intravascular ultrasound (IVUS) is a common imaging modality for diagnosing CVDs. However, an efficient analyzer for IVUS image segmentation is required for assisting cardiologists. In this study, an end-to-end deep-learning convolutional neural network was developed for automatically detecting media-adventitia borders, luminal regions, and calcified plaque in IVUS images. A total of 713 grayscale IVUS images from 18 patients were used as training data for the proposed deep-learning model. The model is constructed using the three modified U-Nets and combined with the concept of cascaded networks to prevent errors in the detection of calcification owing to the interference of pixels outside the plaque regions. Three loss functions (Dice, Tversky, and focal loss) with various characteristics were tested to determine the best setting for the proposed model. The efficacy of the deep-learning model was evaluated by analyzing precision-recall curve. The average precision (AP), Dice score coefficient, precision, sensitivity, and specificity of the predicted and ground truth results were then compared. All training processes were validated using leave-one-subject-out cross-validation. The experimental results showed that the proposed deep-learning model exhibits high performance in segmenting the media-adventitia layers and luminal regions for all loss functions, with all tested metrics being higher than 0.90. For locating calcified tissues, the best result was obtained when the focal loss function was applied to the proposed model, with an AP of 0.73; however, the prediction efficacy was affected by the proportion of calcified tissues within the plaque region when the focal loss function was employed. Compared with commercial software, the proposed method exhibited high accuracy in segmenting IVUS images in some special cases, such as when shadow artifacts or side vessels surrounded the target vessel.

Deep convolutional neural network-based diabetic eye disease detection and classification using thermal images

Infrared thermography which is non-contact and non-invasive technique is widely accepted as a medical diagnostic tool. Thermal images are processed for abnormality detection and quantification. It has been used in the diagnosis of dry eye, meibomian gland dysfunction, thyroid eye disease and glaucoma. Diabetic eye disease (DED) detection using thermal images is an absolutely new attempt. The early detection of the occurrence of DED can be very helpful for clinical treatment. In this paper, we are attempting towards finding an automatic way to classify DEDs in thermal images using a deep learning-based convolutional neural network (CNN) methodology. The sensitivity of 92.30%, specificity of 98.46%, and accuracy of 95.38% on testing dataset with reference to expert's ground truth results are obtained. The results attained evidently exhibit that the thermal imaging is promising modality and proposed deep learning method is capable for automatic diagnosis of diabetic eye disease classification.

CG-SENSE revisited: Results from the first ISMRM reproducibility challenge.

The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of "Advances in sensitivity encoding with arbitrary k-space trajectories" by Pruessmann et al. METHODS: The task of the challenge was to reconstruct radially acquired multicoil k-space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python). Visually, differences between submissions were small. Pixel-wise differences originated from image orientation, assumed field-of-view, or resolution. The reference implementations were in good agreement, both visually and in terms of image similarity metrics. While the description level of the published algorithm enabled participants to reproduce CG-SENSE in general, details of the implementation varied, for example, density compensation or Tikhonov regularization. Implicit assumptions about the data lead to further differences, emphasizing the importance of sufficient metadata accompanying open datasets. Defining reproducibility quantitatively turned out to be nontrivial for this image reconstruction challenge, in the absence of ground-truth results. Typical similarity measures like NMSE of SSIM were misled by image intensity scaling and outlier pixels. Thus, to facilitate reproducibility, researchers are encouraged to publish code and data alongside the original paper. Future methodological papers on MR image reconstruction might benefit from the consolidated reference implementations of CG-SENSE presented here, as a benchmark for methods comparison.

An analysis of correctness for API recommendation: are the unmatched results useless?

API recommendation is a promising approach which is widely used during software development. However, the evaluation of API recommendation is not explored with sufficient rigor. The current evaluation of API recommendation mainly focuses on correctness, the measurement is conducted by matching recommended results with ground-truth results. In most cases, there is only one set of ground-truth APIs for each recommendation attempt, but the object code can be implemented in dozens of ways. The neglect of code diversity results in a possible defect in the evaluation. To address the problem, we invite 15 developers to analyze the unmatched results in a user study. The online evaluation confirms that some unmatched APIs can also benefit to programming due to the functional correlation with ground-truth APIs. Then we measure the API functional correlation based on the relationships extracted from API knowledge graph, API method name, and API documentation. Furthermore, we propose an approach to improve the measurement of correctness based on API functional correlation. Our measurement is evaluated on a dataset of 6141 requirements and historical code fragments from related commits. The results show that 28.2% of unmatched APIs can contribute to correctness in our experiments.

DeepDTI: High-fidelity six-direction diffusion tensor imaging using deep learning

Diffusion tensor magnetic resonance imaging (DTI) is unsurpassed in its ability to map tissue microstructure and structural connectivity in the living human brain. Nonetheless, the angular sampling requirement for DTI leads to long scan times and poses a critical barrier to performing high-quality DTI in routine clinical practice and large-scale research studies. In this work we present a new processing framework for DTI entitled DeepDTI that minimizes the data requirement of DTI to six diffusion-weighted images (DWIs) required by conventional voxel-wise fitting methods for deriving the six unique unknowns in a diffusion tensor using data-driven supervised deep learning. DeepDTI maps the input non-diffusion-weighted (b ​= ​0) image and six DWI volumes sampled along optimized diffusion-encoding directions, along with T1-weighted and T2-weighted image volumes, to the residuals between the input and high-quality output b = 0 image and DWI volumes using a 10-layer three-dimensional convolutional neural network (CNN). The inputs and outputs of DeepDTI are uniquely formulated, which not only enables residual learning to boost CNN performance but also enables tensor fitting of resultant high-quality DWIs to generate orientational DTI metrics for tractography. The very deep CNN used by DeepDTI leverages the redundancy in local and non-local spatial information and across diffusion-encoding directions and image contrasts in the data. The performance of DeepDTI was systematically quantified in terms of the quality of the output images, DTI metrics, DTI-based tractography and tract-specific analysis results. We demonstrate rotationally-invariant and robust estimation of DTI metrics from DeepDTI that are comparable to those obtained with two b ​= ​0 images and 21 DWIs for the primary eigenvector derived from DTI and two b ​= ​0 images and 26–30 DWIs for various scalar metrics derived from DTI, achieving 3.3–4.6 × ​acceleration, and twice as good as those of a state-of-the-art denoising algorithm at the group level. The twenty major white-matter tracts can be accurately identified from the tractography of DeepDTI results. The mean distance between the core of the major white-matter tracts identified from DeepDTI results and those from the ground-truth results using 18 ​b ​= ​0 images and 90 DWIs measures around 1–1.5 ​mm. DeepDTI leverages domain knowledge of diffusion MRI physics and power of deep learning to render DTI, DTI-based tractography, major white-matter tracts identification and tract-specific analysis more feasible for a wider range of neuroscientific and clinical studies.

Quantitative assessment of water content and mineral abundances at Gale crater on Mars with orbital observations

Context. The information of water content can help to improve atmospheric and climate models, and thus provide a better understanding of the past and present role of water and aqueous alteration on Mars. Mineral abundances can provide unique constraints on their formation environments and thus also on the geological and climate evolution of Mars. Aims. In this study, we used a state-of-the-art approach to derive the hydration state and mineral abundances over Gale crater on Mars, analysing hyperspectral visible/near-infrared data from the Observatoire pour la Minéralogie, l’Eau, les Glaces et l’Activité (OMEGA) instrument onboard Mars Express and from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) instrument onboard the Mars Reconnaissance Orbiter. Methods. The Discrete Ordinate Transfer model was used to perform atmospheric and thermal correction of the OMEGA and CRISM hyperspectral data in order to derive the surface single scattering albedos (SSAs) at Gale crater, Mars. Water content was estimated using a linear relationship between the derived effective single-particle absorption thickness at 2.9 μm from SSAs and the water weight percentage. Mineral abundances were retrieved by performing the linear spectral unmixing of SSAs from CRISM data. The results were compared with the ground-truth results returned from Curiosity rover. Results. The water content for most areas at Gale crater derived using the OMEGA data is around 2–3 water weight percent (water wt % hereafter), which is in agreement with that derived from the in situ measurements by Curiosity’s Sample Analysis at Mars instrument. However, the sensitivity tests show that uncertainties exist due to the combination of several factors including modelling bias, instrumental issue, and different sensing techniques. The derived mineral abundances using the orbital data are not fully consistent with that derived by Curiosity, and the discrepancy may be due to a combination of dust cover, texture, and particle size effects, as well as the effectiveness of the quantitative model. Conclusions. The ground-truth data from Curiosity provide a critical calibration point for the quantitative method used in the orbital remote-sensing observations. Our analysis indicates that the method presented here has great potential for mapping the water content and mineral abundances on Mars, but caution must be taken when using these abundance results for geological interpretations.

Adaptive BRDF‐Oriented Multiple Importance Sampling of Many Lights

AbstractMany‐light rendering is becoming more common and important as rendering goes into the next level of complexity. However, to calculate the illumination under many lights, state of the art algorithms are still far from efficient, due to the separate consideration of light sampling and BRDF sampling. To deal with the inefficiency of many‐light rendering, we present a novel light sampling method named BRDF‐oriented light sampling, which selects lights based on importance values estimated using the BRDF's contributions. Our BRDF‐oriented light sampling method works naturally with MIS, and allows us to dynamically determine the number of samples allocated for different sampling techniques. With our method, we can achieve a significantly faster convergence to the ground truth results, both perceptually and numerically, as compared to previous many‐light rendering algorithms.

DECISION TREES FOR SEGMENTATION AND ASSESSMENT OF WOUND HEALING PROCESS OF DIABETIC PATIENTS

Wound healing is a slow process in diabetic patients due to levels of insulin variations in the body. Thus, we present an automated system to analyze and assess different stages of wound healing process of diabetic patients. The diabetic wound healing stages have been defined into three types, such as the level of tissues present in the wound: The percentage of granulations tissues, Necrotic tissues and Slough tissues present in the diabetic patients. The performance of the proposed method shall be assessed based on the clear accuracy of segmentation of wound region present in the patient body. The Decision Tree-based Segmentation method has yielded a good segmentation accuracy of 98.32% in comparison with the ground truth results of clinical data. Further, the assessment of wound healing stages of proposed method has given a good accuracy of measuring the stages of diabetic patients by measuring the percentage of different types of tissues present in the wound region. Based on the results of classification accuracy of the proposed method, we assess whether the wound is going to heal quickly or not. Thus, we presented an algorithm of Decision Trees for the purpose of segmentation and assessment of wound healing process of diabetic patients.

A smart system of 3D liver tumour segmentation

An efficient segmentation of liver tumour and volumetry facilitates medical experts to resolve the rate of tumour growth and follow up treatment. In this paper, a smart system for semi-automatic segmentation of liver tumour from medical images based on Geodesic active contours using level set method is proposed. The system passes through following stages. After the image is loaded, the Region of Interest (ROI) is selected and the Geodesic Active Contours with level set methods are initialised. Then segmentation result is updated to extract the tumour in three dimensions. The liver tumours detected by the system were compared with those delineated manually by experts, used as the ground truth results. The system was evaluated on Computed Tomography images of different datasets of tumours and compared with other methods. The result of proposed system obtained the Dice SC 0.948 and Jaccard SC 0.902 exhibits the steadfastness and effectiveness of the system.

A smart system of 3D liver tumour segmentation

An efficient segmentation of liver tumour and volumetry facilitates medical experts to resolve the rate of tumour growth and follow up treatment. In this paper, a smart system for semi-automatic segmentation of liver tumour from medical images based on Geodesic active contours using level set method is proposed. The system passes through following stages. After the image is loaded, the Region of Interest (ROI) is selected and the Geodesic Active Contours with level set methods are initialised. Then segmentation result is updated to extract the tumour in three dimensions. The liver tumours detected by the system were compared with those delineated manually by experts, used as the ground truth results. The system was evaluated on Computed Tomography images of different datasets of tumours and compared with other methods. The result of proposed system obtained the Dice SC 0.948 and Jaccard SC 0.902 exhibits the steadfastness and effectiveness of the system.