Semi-automatic Segmentation Method Research Articles

A method framework of semi-automatic knee bone segmentation and reconstruction from computed tomography (CT) images.

Accurate delineation of knee bone boundaries is crucial for computer-aided diagnosis (CAD) and effective treatment planning in knee diseases. Current methods often struggle with precise segmentation due to the knee joint's complexity, which includes intricate bone structures and overlapping soft tissues. These challenges are further complicated by variations in patient anatomy and image quality, highlighting the need for improved techniques. This paper presents a novel semi-automatic segmentation method for extracting knee bones from sequential computed tomography (CT) images. Our approach integrates the fuzzy C-means (FCM) algorithm with an adaptive region-based active contour model (ACM). Initially, the FCM algorithm assigns membership degrees to each voxel, distinguishing bone regions from surrounding soft tissues based on their likelihood of belonging to specific bone regions. Subsequently, the adaptive region-based ACM utilizes these membership degrees to guide the contour evolution and refine segmentation boundaries. To ensure clinical applicability, we further enhance our method using the marching cubes algorithm to reconstruct a three-dimensional (3D) model. We evaluated the method on six randomly selected knee joints. We evaluated the method using quantitative metrics such as the Dice coefficient, sensitivity, specificity, and geometrical assessment. Our method achieved high Dice scores for the femur (98.95%), tibia (98.10%), and patella (97.14%), demonstrating superior accuracy. Remarkably low root mean square distance (RSD) values were obtained for the tibia and femur (0.5±0.14 mm) and patella (0.6±0.13 mm), indicating precise segmentation. The proposed method offers significant advancements in CAD systems for knee pathologies. Our approach demonstrates superior performance in achieving precise and accurate segmentation of knee bones, providing valuable insights for anatomical analysis, surgical planning, and patient-specific prostheses.

Accuracy of deep learning-based upper airway segmentation

IntroductionIn orthodontic treatments, accurately assessing the upper airway volume and morphology is essential for proper diagnosis and planning. Cone beam computed tomography (CBCT) is used for assessing upper airway volume through manual, semi-automatic, and automatic airway segmentation methods. This study evaluates upper airway segmentation accuracy by comparing the results of an automatic model and a semi-automatic method against the gold standard manual method. Materials and methodsAn automatic segmentation model was trained using the MONAI Label framework to segment the upper airway from CBCT images. An open-source program, ITK-SNAP, was used for semi-automatic segmentation. The accuracy of both methods was evaluated against manual segmentations. Evaluation metrics included Dice Similarity Coefficient (DSC), Precision, Recall, 95% Hausdorff Distance (HD), and volumetric differences. ResultsThe automatic segmentation group averaged a DSC score of 0.915±0.041, while the semi-automatic group scored 0.940±0.021, indicating clinically acceptable accuracy for both methods. Analysis of the 95% HD revealed that semi-automatic segmentation (0.997±0.585) was more accurate and closer to manual segmentation than automatic segmentation (1.447±0.674). Volumetric comparisons revealed no statistically significant differences between automatic and manual segmentation for total, oropharyngeal, and velopharyngeal airway volumes. Similarly, no significant differences were noted between the semi-automatic and manual methods across these regions. ConclusionIt has been observed that both automatic and semi-automatic methods, which utilise open-source software, align effectively with manual segmentation. Implementing these methods can aid in decision-making by allowing faster and easier upper airway segmentation with comparable accuracy in orthodontic practice.

PET radiomics-based lymphovascular invasion prediction in lung cancer using multiple segmentation and multi-machine learning algorithms.

The current study aimed to predict lymphovascular invasion (LVI) using multiple machine learning algorithms and multi-segmentation positron emission tomography (PET) radiomics in non-small cell lung cancer (NSCLC) patients, offering new avenues for personalized treatment strategies and improving patient outcomes. One hundred and twenty-six patients with NSCLC were enrolled in this study. Various automated and semi-automated PET image segmentation methods were applied, including Local Active Contour (LAC), Fuzzy-C-mean (FCM), K-means (KM), Watershed, Region Growing (RG), and Iterative thresholding (IT) with different percentages of the threshold. One hundred five radiomic features were extracted from each region of interest (ROI). Multiple feature selection methods, including Minimum Redundancy Maximum Relevance (MRMR), Recursive Feature Elimination (RFE), and Boruta, and multiple classifiers, including Multilayer Perceptron (MLP), Logistic Regression (LR), XGBoost (XGB), Naive Bayes (NB), and Random Forest (RF), were employed. Synthetic Minority Oversampling Technique (SMOTE) was also used to determine if it boosts the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Our results indicated that the combination of SMOTE, IT (with 45% threshold), RFE feature selection and LR classifier showed the best performance (AUC = 0.93, ACC = 0.84, SEN = 0.85, SPE = 0.84) followed by SMOTE, FCM segmentation, MRMR feature selection, and LR classifier (AUC = 0.92, ACC = 0.87, SEN = 1, SPE = 0.84). The highest ACC belonged to the IT segmentation (with 45 and 50% thresholds) alongside Boruta feature selection and the NB classifier without SMOTE (ACC = 0.9, AUC = 0.78 and 0.76, SEN = 0.7, and SPE = 0.94, respectively). Our results indicate that selection of appropriate segmentation method and machine learning algorithm may be helpful in successful prediction of LVI in patients with NSCLC with high accuracy using PET radiomics analysis.

Postmortem Computed Tomography – Based Body Weight Estimation in Korean Infants Using Volume and Multiplication Factors

Postmortem computed tomography (PMCT) is used in forensic medicine worldwide due to its ability to non-invasively visualize injuries, hemorrhage, and estimate volume. In the autopsy of infants, assessing nutritional conditions such as weight is crucial for identifying neglect. This study aims to evaluate the usefulness of retrospectively estimating the weight of Korean infants using PMCT-based volume and multiplication factors, even when the body has been cremated. A total of 44 cases of infant death (under 12 months) were analyzed. PMCT images were obtained before autopsy. Autopsy records and documentation provided by the police at the time of autopsy were reviewed to determine the weight (g) of the infant. PMCT-based infant volumes (mL) were estimated using a three-dimensional semi-automatic segmentation method. Multiplication factors (g/mL) were calculated by dividing the weight recorded at autopsy by the PMCT-based volume, yielding a mean of 1.047 g/mL, ranging from 1.014 g/mL to 1.085 g/mL. The mean absolute error compared to weights recorded at autopsy was 95 g. Significant discrepancies were observed between weights recorded at the scene or medical center and those measured at autopsy. This study demonstrates that PMCT-based weight estimation for Korean infants is a reliable method and has the potential for retrospectively validating incorrect weight measurements and addressing inconsistencies in recorded weight data.

One-click annotation to improve segmentation by a convolutional neural network for PET images of head and neck cancer patients

A convolutional neural network (CNN) can be used to perform fully automatic tumor segmentation from the positron emission tomography (PET) images of head and neck cancer patients but the predictions often contain false positive segmentation caused by the high concentration of the tracer substance in the human brain. A potential solution would be a one-click annotation in which a user points the location of the tumor by clicking the image. This information can then be given either directly to a CNN or an algorithm that fixes its predictions. In this article, we compare the fully automatic segmentation to four semi-automatic approaches by using 962 transaxial slices collected from the PET images of 100 head and neck cancer patients. According to our results, a semi-automatic segmentation method with information about the center of the tumor performs the best with a median Dice score of 0.708.

AB038. Establishing a reliable semi-automated segmentation method for assessing chemoradiotherapy effects on the non-tumoral brain.

Current voxel-based morphometry (VBM) studies of chemoradiotherapy effects on healthy tissues of the glioblastoma multiforme (GBM) brain face a challenge with neuroanatomical distortions (tumor, tumor edema, and resection cavities) and limited comparisons can be drawn across studies due to lack of a universally accepted software package. Our aim is to compare current semi-automated segmentation methods and optimize them for reliability in investigating the effects of chemoradiotherapy on GBM patients. A publicly available dataset was used based on predefined inclusion and exclusion criteria. VBM pipelines CAT12 and FSL were tested and optimized to reduce the impact of neuroanatomical distortions. T1-weighted images were screened, and post-processed with FSL and CAT12. Gray matter (GM) and white matter (WM), and cerebrospinal fluid (CSF) volumes of whole brain, tumour-containing and non-tumor containing hemispheres, pre- and post-chemoradiotherapy were calculated and analyzed with Wilcoxon signed-rank tests. Agreement and consistency between FSL and CAT12 were assessed using Bland-Altman plots and intraclass correlation coefficients (ICCs). Post-chemoradiotherapy GM volumes were significantly reduced in whole brain with a compensatory significant increase in CSF volumes, while WM volumes had no significant changes. Similar trends were noted in tumor-containing and non-tumor-containing hemispheres. Bland-Altman plots showed good agreement between FSL and CAT12 processed GM and WM volumes of whole brain, tumor-containing, and non-tumor-containing hemispheres. ICC ≥0.70 was observed in GM [0.70 (0.53-0.82)] and WM [0.75 (0.60-0.85)] volumes of non-tumor-containing hemisphere, and WM [0.71 (0.55-0.83)] volumes of whole brain. GM volumes of tumor-containing hemisphere had good agreement but surprisingly, poor consistency [0.50 (0.25-0.68)]. CSF volumes in non-tumor-containing hemisphere had better agreement and consistency [0.55 (0.32-0.71)] than whole brain [0.49 (0.25-0.67)] and tumor-containing hemisphere CSF [0.36 (0.10-0.58)] volumes. Visual inspection revealed both CAT12 and FSL mis-segmented in the presence of neuroanatomical distortion although CAT12 was more susceptible in the presence of a hematoma. VBM studies of chemoradiotherapy effects on the brain post-tumor resection remain challenging due to neuroanatomical distortions. A reliable alternative is to use non-tumor-containing hemispheres with no anatomical distortion. Should tumor-containing brains be used, FSL is a more suitable choice, especially in the presence of hematoma distortion.

Metabolic tumour area: a novel prognostic indicator based on 18F-FDG PET/CT in patients with diffuse large B-cell lymphoma in the R-CHOP era

BackgroundThe metabolic tumour area (MTA) was found to be a promising predictor of prostate cancer. However, the role of MTA based on 18F-FDG PET/CT in diffuse large B-cell lymphoma (DLBCL) prognosis remains unclear. This study aimed to elucidate the prognostic significance of MTA and evaluate its incremental value to the National Comprehensive Cancer Network International Prognostic Index (NCCN-IPI) for DLBCL patients treated with first-line R-CHOP regimens.MethodsA total of 280 consecutive patients with newly diagnosed DLBCL and baseline 18F-FDG PET/CT data were retrospectively evaluated. Lesions were delineated via a semiautomated segmentation method based on a 41% SUVmax threshold to estimate semiquantitative metabolic parameters such as total metabolic tumour volume (TMTV) and MTA. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal cut-off values. Progression-free survival (PFS) and overall survival (OS) were the endpoints that were used to evaluate the prognosis. PFS and OS were estimated via Kaplan‒Meier curves and compared via the log-rank test.ResultsUnivariate analysis revealed that patients with high MTA, high TMTV and NCCN-IPI ≥ 4 were associated with inferior PFS and OS (P < 0.0001 for all). Multivariate analysis indicated that MTA remained an independent predictor of PFS and OS [hazard ratio (HR), 2.506; 95% confidence interval (CI), 1.337–4.696; P = 0.004; and HR, 1.823; 95% CI, 1.005–3.310; P = 0.048], whereas TMTV was not. Further analysis using the NCCN-IPI model as a covariate revealed that MTA and NCCN-IPI were still independent predictors of PFS (HR, 2.617; 95% CI, 1.494–4.586; P = 0.001; and HR, 2.633; 95% CI, 1.650–4.203; P < 0.0001) and OS (HR, 2.021; 95% CI, 1.201–3.401; P = 0.008; and HR, 3.869; 95% CI, 1.959–7.640; P < 0.0001; respectively). Furthermore, MTA was used to separate patients with high NCCN-IPI risk scores into two groups with significantly different outcomes.ConclusionsPre-treatment MTA based on 18F-FDG PET/CT and NCCN-IPI were independent predictor of PFS and OS in DLBCL patients treated with R-CHOP. MTA has additional predictive value for the prognosis of patients with DLBCL, especially in high-risk patients with NCCN-IPI ≥ 4. In addition, the combination of MTA and NCCN-IPI may be helpful in further improving risk stratification and guiding individualised treatment options.Trial registrationThis research was retrospectively registered with the Ethics Committee of the Third Affiliated Hospital of Soochow University, and the registration number was approval No. 155 (approved date: 31 May 2022).

A Semiautomatic Image Processing-Based Method for Binary Segmentation of Lungs in Computed Tomography Images

A Semiautomatic Image Processing-Based Method for Binary Segmentation of Lungs in Computed Tomography Images

Diagnostic Accuracy of Posterior/Anterior Periventricular WMH Ratio to Differentiate CAA From Hypertensive Arteriopathy.

Periventricular white matter hyperintensities (PVWMHs) in cerebral amyloid angiopathy (CAA) have been reported posterior predominant using semiautomated segmentation method and logarithmic transformation. We aimed to compare PVWMH extent and posterior/anterior distribution between patients with CAA and patients with hypertensive arteriopathy with radiological tools available in daily practice. We retrospectively analyzed confluent PVWMH directly adjacent to lateral ventricles on axial FLAIR (fluid-attenuated inversion recovery) from 108 patients with CAA and 99 patients with hypertensive arteriopathy presenting with hemorrhage-related symptoms consecutively recruited in our stroke database (Nîmes University Hospital, France) between January 2015 and March 2022. For each of the left (L), right (R), anterior (A), and posterior (P) horns of lateral ventricles, the maximal distance between the outer PVWMH border and ventricle border was measured. The sum of anterior left PVWMH and anterior right PVWMH, and posterior left PVWMH and posterior right PVWMH resulted in anterior and posterior extent, respectively. Compared with hypertensive arteriopathy, patients with CAA were older (median, 77 versus 71; P=0.0010) and less frequently male (46% versus 64%; P=0.012) and had less frequent hypertension (45% versus 63%; P=0.013) and more chronic hemorrhages (P<0.0001). CAA showed slightly more extensive anterior right PVWMH (median, 6.50 versus 5.90 mm; P=0.034), far more extensive (all P<0.0001) posterior left PVWMH (median, 13.95 versus 6.95 mm), posterior right PVWMH (median, 14.15 versus 5.45 mm), posterior (median, 27.95 versus 13.00 mm), and total (median, 39.60 versus 24.65 mm) PVWMH, and higher posterior/anterior ratios (median, 1.82 versus 1.01). Age-/sex-adjusted model predicting CAA incorporating total PVWMH extent and posterior/anterior ratios for the given score (-4.3683+0.0268×PVWMH-T+0.3749×posterior/anterior PVWMH ratio+0.0394×age+0.3046 when female) showed highest area under the curve (0.76 [0.70-0.83]), with a 72% [62.50-80.99] sensitivity and 76% [67.18-84.12] specificity. Values above the optimal threshold of 0.22 for the score showed a crude relative risk of 2.75 (2.26-2.37; P<0.0001). Severe posterior PVWMH and high posterior/anterior PVWMH ratio assessed by radiological tools used in daily practice are associated with probable CAA versus hypertensive arteriopathy. URL: https://www.clinicaltrials.gov; Unique identifier: NCT05486897.

Immunoexpression of IL-33 in the different clinical aspects of canine atopic dermatitis

Canine atopic dermatitis (CAD) is a chronic and inflammatory skin condition with a multifaceted origin, involving genetic factors, skin barrier abnormalities, immune responses, and hypersensitivity to various allergens. Interleukin 33 (IL-33), released by keratinocytes upon cellular injury, plays a crucial role in atopic dermatitis pathogenesis by inducing Th2 lymphocyte-mediated immune responses. This study aimed to evaluate IL-33 expression in dogs with atopic dermatitis and compare it to a control group. Forty-nine dogs were included, with 39 having atopic dermatitis, subdivided into groups based on clinical characteristics, and ten in the control group. Lesion and pruritus scores were assessed, and incisional biopsies were analyzed for dermatopathological characteristics. IL-33 expression was evaluated using immunohistochemistry, the analyses were blinded, based on the measurement of immunostaining areas using Image Pro-Plus software, version 4.5, relying on a semi-automatic color segmentation method, where the tissue immunostaining area for each biomarker was artificially delimited and quantified. Statistically significant differences in IL-33 immunostaining were found among groups (P=0.0005). Lichenified dogs (group 4) exhibited higher immunostaining compared to erythema (group 3) (P=0.0006), alesional pruritus (group 2) (P=0.0261), and the control group (group 1) (P=0.0079). IL-33 immunostaining increased with lesion progression, strongly correlating with lesion scores (P<0.0001), particularly in patients with chronic lesions characterized by erythema and lichenification. These findings suggest IL-33's significant role in canine atopic dermatitis pathogenesis and its association with lesion and inflammation scores during the chronic phase. This suggests potential therapeutic interventions targeting IL-33 or its receptors, though further studies are needed to explore these possibilities.

An open-source, three-dimensional growth model of the mandible

The available reference data for the mandible and mandibular growth consists primarily of two-dimensional linear or angular measurements. The aim of this study was to create the first open-source, three-dimensional statistical shape model of the mandible that spans the complete growth period. Computed tomography scans of 678 mandibles from children and young adults between 0 and 22 years old were included in the model. The mandibles were segmented using a semi-automatic or automatic (artificial intelligence-based) segmentation method. Point correspondence among the samples was achieved by rigid registration, followed by non-rigid registration of a symmetrical template onto each sample. The registration process was validated with adequate results. Principal component analysis was used to gain insight in the variation within the dataset and to investigate age-related changes and sexual dimorphism. The presented growth model is accessible globally and free-of-charge for scientists, physicians and forensic investigators for any kind of purpose deemed suitable. The versatility of the model opens up new possibilities in the fields of oral and maxillofacial surgery, forensic sciences or biological anthropology. In clinical settings, the model may aid diagnostic decision-making, treatment planning and treatment evaluation.

Comparison of semi-automatic and manual segmentation methods for tumor delineation on head and neck squamous cell carcinoma (HNSCC) positron emission tomography (PET) images

Objective. Accurate and reproducible tumor delineation on positron emission tomography (PET) images is required to validate predictive and prognostic models based on PET radiomic features. Manual segmentation of tumors is time-consuming whereas semi-automatic methods are easily implementable and inexpensive. This study assessed the reliability of semi-automatic segmentation methods over manual segmentation for tumor delineation in head and neck squamous cell carcinoma (HNSCC) PET images. Approach. We employed manual and six semi-automatic segmentation methods (just enough interaction (JEI), watershed, grow from seeds (GfS), flood filling (FF), 30% SUVmax and 40%SUVmax threshold) using 3D slicer software to extract 128 radiomic features from FDG-PET images of 100 HNSCC patients independently by three operators. We assessed the distributional properties of all features and considered 92 log-transformed features for subsequent analysis. For each paired comparison of a feature, we fitted a separate linear mixed effect model using the method (two levels; manual versus one semi-automatic method) as a fixed effect and the subject and the operator as the random effects. We estimated different statistics—the intraclass correlation coefficient agreement (aICC), limits of agreement (LoA), total deviation index (TDI), coverage probability (CP) and coefficient of individual agreement (CIA)—to evaluate the agreement between the manual and semi-automatic methods. Main results. Accounting for all statistics across 92 features, the JEI method consistently demonstrated acceptable agreement with the manual method, with median values of aICC = 0.86, TDI = 0.94, CP = 0.66, and CIA = 0.91. Significance. This study demonstrated that JEI method is a reliable semi-automatic method for tumor delineation on HNSCC PET images.

A Growth Prediction Model of Pulmonary Ground-Glass Nodules Based on Clinical Visualization Parameters

Objective To establish a model for predicting the growth of pulmonary ground-glass nodules (GGN) based on the clinical visualization parameters extracted by the 3D reconstruction technique and to verify the prediction performance of the model. Methods A retrospective analysis was carried out for 354 cases of pulmonary GGN followed up regularly in the outpatient of pulmonary nodules in Zhoushan Hospital of Zhejiang Province from March 2015 to December 2022.The semi-automatic segmentation method of 3D Slicer was employed to extract the quantitative imaging features of nodules.According to the follow-up results,the nodules were classified into a resting group and a growing group.Furthermore,the nodules were classified into a training set and a test set by the simple random method at a ratio of 7∶3.Clinical and imaging parameters were used to establish a prediction model,and the prediction performance of the model was tested on the validation set. Results A total of 119 males and 235 females were included,with a median age of 55.0 (47.0,63.0) years and the mean follow-up of (48.4±16.3) months.There were 247 cases in the training set and 107 cases in the test set.The binary Logistic regression analysis showed that age (95%CI=1.010-1.092,P=0.015) and mass (95%CI=1.002-1.067,P=0.035) were independent predictors of nodular growth.The mass (M) of nodules was calculated according to the formula M=V×(CTmean+1000)×0.001 (where V is the volume,V=3/4πR3,R:radius).Therefore,the logit prediction model was established as ln[P/(1-P)]=-1.300+0.043×age+0.257×two-dimensional diameter+0.007×CTmean.The Hosmer-Lemeshow goodness of fit test was performed to test the fitting degree of the model for the measured data in the validation set (χ2=4.515,P=0.808).The check plot was established for the prediction model,which showed the area under receiver-operating characteristic curve being 0.702. Conclusions The results of this study indicate that patient age and nodule mass are independent risk factors for promoting the growth of pulmonary GGN.A model for predicting the growth possibility of GGN is established and evaluated,which provides a basis for the formulation of GGN management strategies.

Development and validation of a semi-automated and unsupervised method for femur segmentation from CT

Quantitative computed tomography (QCT)-based in silico models have demonstrated improved accuracy in predicting hip fractures with respect to the current gold standard, the areal bone mineral density. These models require that the femur bone is segmented as a first step. This task can be challenging, and in fact, it is often almost fully manual, which is time-consuming, operator-dependent, and hard to reproduce. This work proposes a semi-automated procedure for femur bone segmentation from CT images. The proposed procedure is based on the bone and joint enhancement filter and graph-cut algorithms. The semi-automated procedure performances were assessed on 10 subjects through comparison with the standard manual segmentation. Metrics based on the femur geometries and the risk of fracture assessed in silico resulting from the two segmentation procedures were considered. The average Hausdorff distance (0.03 ± 0.01 mm) and the difference union ratio (0.06 ± 0.02) metrics computed between the manual and semi-automated segmentations were significantly higher than those computed within the manual segmentations (0.01 ± 0.01 mm and 0.03 ± 0.02). Besides, a blind qualitative evaluation revealed that the semi-automated procedure was significantly superior (p < 0.001) to the manual one in terms of fidelity to the CT. As for the hip fracture risk assessed in silico starting from both segmentations, no significant difference emerged between the two (R2 = 0.99). The proposed semi-automated segmentation procedure overcomes the manual one, shortening the segmentation time and providing a better segmentation. The method could be employed within CT-based in silico methodologies and to segment large volumes of images to train and test fully automated and supervised segmentation methods.

Automatic and semi-automatic segmentation method of post-myocardial infarction according to magnetic resonance imaging with late gadolinium enhancement

Automatic and semi-automatic segmentation method of post-myocardial infarction according to magnetic resonance imaging with late gadolinium enhancement

Computed tomography-based body composition parameters can predict short-term prognosis in ulcerative colitis patients.

Emerging evidence suggests a potential relationship between body composition and short-term prognosis of ulcerative colitis (UC). Early and accurate assessment of rapid remission based on conventional therapy via abdominal computed tomography (CT) images has rarely been investigated. This study aimed to build a prediction model using CT-based body composition parameters for UC risk stratification. In total, 138 patients with abdominal CT images were enrolled. Eleven quantitative parameters related to body composition involving skeletal muscle mass, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) were measured and calculated using a semi-automated segmentation method. A prediction model was established with significant parameters using a multivariable logistic regression. The receiver operating characteristic (ROC) curve was plotted to evaluate prediction performance. Subgroup analyses were implemented to evaluate the diagnostic efficiency of the prediction model between different disease locations, centers, and CT scanners. The Delong test was used for statistical comparison of ROC curves. VAT density, SAT density, gender, and visceral obesity were significantly statistically different between remission and invalidation groups (all p < 0.05). The accuracy, sensitivity, specificity, and area under the ROC curve (AUC) of the prediction model were 82.61%, 95.45%, 69.89%, and 0.855 (0.792-0.917), respectively. The positive predictive value and negative predictive value were 70.79% and 93.88%, respectively. No significant differences in the AUC of the prediction model were found in different subgroups (all p > 0.05). The predicting model constructed with CT-based body composition parameters is a potential non-invasive approach for short-term prognosis identification and risk stratification. Additionally, VAT density was an independent predictor for escalating therapeutic regimens in UC cohorts. The CT images were used for evaluating body composition and risk stratification of ulcerative colitis patients, and a potential non-invasive prediction model was constructed to identify non-responders with conventional therapy for making therapeutic regimens timely and accurately. • CT-based prediction models help divide patients into invalidation and remission groups in UC. • Results of the subgroup analysis confirmed the stability of the prediction model with a high AUC (all > 0.820). • The visceral adipose tissue density was an independent predictor of bad short-term prognosis in UC.

20. IMPLEMENTATION OF A SEMI-AUTOMATED SEGMENTATION TOOL FOR COLON SURGICAL PLANNING

Abstract Introduction This study outlines the design and implementation of an application focused on optimizing surgical planning for colon surgery through the automated segmentation process of Computed Axial Tomography (CT) images. The tool provides continuous assistance to the user, enhancing the overall process management and efficiency. Methods A module has been developed for the 3DSlicer image computing platform. The algorithm was programmed in Python, primarily utilizing the Slicer, MRML, and VTK libraries. Additionally, DICOM images obtained from a publicly available database were used as samples for the study. Results The implementation of the semi-automatic segmentation method has proved to be successful, achieving the creation of a three-dimensional (3D) model of the colon with satisfactory results. The user has the facility to upload a CT image from a DICOM study, and through few interactions, a 3D model of the colon is generated, optimizing the surgical planning process. Conclusion Although we have successfully developed a semi-automatic segmentation method, the process requires certain specific conditions. For the image to be interpreted correctly, the patient must be treated with iodinated contrast medium prior to the CT study. In addition, an insufflation of the colon should be performed. These prerequisites are essential to ensure the quality and accuracy of the resulting images, which in turn will impact the efficiency of the planning process.

Deep learning-based segmentation of breast masses using convolutional neural networks

Automatic breast tumor segmentation based on convolutional neural networks (CNNs) is significant for the diagnosis and monitoring of breast cancers. CNNs have become an important method for early diagnosis of breast cancer and, thus, can help decrease the mortality rate. In order to assist medical professionals in breast cancer investigation a computerized system based on two encoder-decoder architectures for breast tumor segmentation has been developed. Two pre-trained DeepLabV3+ and U-Net models are proposed. The encoder generates a high-dimensional feature vector while the decoder analyses the low-resolution feature vector provided by the encoder and generates a semantic segmentation mask. Semantic segmentation based on deep learning techniques can overcome the limitations of traditional algorithms. To assess the efficiency of breast ultrasound image segmentation, we compare the segmentation results provided by CNNs against the Local Graph Cut technique (a semi-automatic segmentation method) in the Image Segmenter application. The output segmentation results have been evaluated by using the Dice similarity coefficient that compares the ground truth images provided by the specialists against the predicted segmentation results provided by the CNNs and Local Graph Cut algorithm. The proposed approach is validated on 780 breast ultrasonographic images of the BUSI public database of which 437 are benign and 210 are malignant. The BUSI database provides classification (benign or malignant) labels for ground truth in binary mask images. The average Dice scores computed between the ground truth images against CNNs were as follows: 0.9360 (malignant) and 0.9325 (benign) for the DeepLabV3+ architecture and of 0.6251 (malignant) and 0.6252 (benign) for the U-Net, respectively. When the segmentation results provided by CNNs were compared with the Local Graph Cut segmented images, the Dice scores were 0.9377 (malignant) and 0.9204 (benign) for DeepLabV3+ architecture and 0.6115 (malignant) and 0.6119 (benign) for U-Net, respectively. The results show that the DeepLabV3+ has significantly better segmentation performance and outperforms the U-Net network.

Robust semi-automatic segmentation method: an expert assistant tool for muscles in CT and MR data

ABSTRACT Image muscle segmentation is useful to quantitatively assess musculoskeletal diseases by extracting biomarkers such as shape, texture and water diffusivity metrics. Although volumetric manual segmentation is time consuming and a bottleneck in practice, fully automatic approaches are still in progress to reach an acceptable accuracy. In this paper, we provide a robust semi-automated tool to segment two musculoskeletal systems, i.e. thigh and shoulder in MRI and CT modalities, respectively. The tool only needs a few manually labelled cross-sections to build a directed graph-structure of corresponding points between the successive spaced slices. The boundaries of each muscle are obtained by performing a spline interpolation based on the directed graph-structure. Each muscle label and its corresponding 3D mesh are deduced using post-processing techniques. We evaluated the tool on 26 MRI thighs and 16 CT shoulders. Three metrics along with inter-muscle overlapping were employed to evaluate the tool by comparison to an expert manual segmentation and a publicly available tools (ITK-SNAP, 3D Slicer). The results showed a mean Dice 0.988 ± 0.003 , and Hausdorff Distance 4.86 ± 1.67 mm in comparison to the manual reference for thigh muscle segmentation, and a mean Dice 0.961 ± 0.005 and Hausdorff Distance 2.42 ± 0.79 mm for shoulder muscle segmentation, outperformed the other methods. The tool is proposed as slicer module available at https://github.com/latimagine/SlicerSpline.

GPU-accelerated lung CT segmentation based on level sets and texture analysis

This paper presents a novel semi-automatic method for lung segmentation in thoracic CT datasets. The fully three-dimensional algorithm is based on a level set representation of an active surface and integrates texture features to improve its robustness. The method’s performance is enhanced by the graphics processing unit (GPU) acceleration. The segmentation process starts with a manual initialisation of 2D contours on a few representative slices of the analysed volume. Next, the starting regions for the active surface are generated according to the probability maps of texture features. The active surface is then evolved to give the final segmentation result. The recent implementation employs features based on grey-level co-occurrence matrices and Gabor filters. The algorithm was evaluated on real medical imaging data from the LCTCS 2017 challenge. The results were also compared with the outcomes of other segmentation methods. The proposed approach provided high segmentation accuracy while offering very competitive performance.