CT Scan Images Research Articles

CT Scan Based Estimation of Distance of Human Paracentral Lobule from Different Landmarks of Cerebral Hemisphere between Bangladeshi Male and Female Population

Context: This study was carried out to observe the distance of paracentral lobule in relation to different points of cerebral hemisphere between adult male and female Bangladeshi population. Materials & Methods: This cross sectional study was conducted in the Department of Anatomy, Dhaka Medical College, Dhaka, during the period of January 2017 to December 2017. A total of 70 adult Bangladeshi male & female were selected, age ranging from 20-65 years. CT scan image of brain in midsagittal view was used for this study. Distance of paracentral lobule in relation to different points of cerebral hemisphere was measured and compared between adult male and female Bangladeshi population. Calculation was done using unpaired student’s ‘t’ test. Results: In male, mean distance from inferior most point of paracentral lobule to corpus callosum were 11.37±1.79 mm and 11.09±1.87 mm on right and left cerebral hemisphere respectively. In female, mean distance were 11.67±2.36 and 11.20±2.38 mm on right and left respectively. In male, distance from frontal pole to anterior most point of paracentral lobule of right and left were 89.21±5.22 mm and 88.82±5.28 mm respectively. In female, on right and left cerebral hemisphere, distance were 87.81±3.57 mm and 87.23±3.60 mm respectively. In male, distance from occipital pole to posterior most point of paracentral lobule of right and left cerebral hemisphere were 73.11±3.38 mm and 72.68±3.24 mm respectively. In female on right and left, distance were 71.86±4.67 mm and 71.49±5.08 mm respectively. Conclusion: There was no statistically significant difference regarding distance of paracentral lobule from different landmarks of cerebral hemisphere between adult male and female of Bangladesh. Bangladesh Journal of Anatomy July 2021, Vol. 19, No. 1, pp. 8-12

Comparative study of DCNN and image processing based classification of chest X-rays for identification of COVID-19 patients using fine-tuning

The conventional detection of COVID-19 by evaluating the CT scan images is tiresome, often experiences high inter-observer variability and uncertainty issues. This work proposes the automatic detection and classification of COVID-19 by analysing the chest X-ray images (CXR) with the deep convolutional neural network (DCNN) models through a fine-tuning and pre-training approach. CXR images pertaining to four health scenarios, namely, healthy, COVID-19, bacterial pneumonia and viral pneumonia, are considered and subjected to data augmentation. Two types of input datasets are prepared; in which dataset I contains the original image dataset categorised under four classes, whereas the original CXR images are subjected to image pre-processing via Contrast Limited Adaptive Histogram Equalisation (CLAHE) algorithm and Blackhat Morphological Operation (BMO) for devising the input dataset II. Both datasets are supplied as input to various DCNN models such as DenseNet, MobileNet, ResNet, VGG16, and Xception for achieving multi-class classification. It is observed that the classification accuracies are improved, and the classification errors are reduced with the image pre-processing. Overall, the VGG16 model resulted in better classification accuracies and reduced classification errors while accomplishing multi-class classification. Thus, the proposed work would assist the clinical diagnosis, and reduce the workload of the front-line healthcare workforce and medical professionals.

Prediction of Brain Cancer Occurrence and Risk Assessment of Brain Hemorrhage Using Hybrid Deep Learning Technique

The prediction of brain cancer occurrence and risk assessment of brain hemorrhage using a hybrid deep learning (DL) technique is a critical area of research in medical imaging analysis. One prominent challenge in this field is the accurate identification and classification of brain tumors and hemorrhages, which can significantly impact patient prognosis and treatment planning. The objectives of the study address the prediction of brain cancer occurrence and the assessment of risk levels associated with both brain cancers due to brain hemorrhage. A diverse dataset of brain MRI and CT scan images. Utilize Unsymmetrical Trimmed Median Filter with Optics Clustering for noise removal while preserving edges and details. The Chan-Vese segmentation process for refined segmentation. Brain cancer detection using Multi-Head Self-Attention Dilated Convolution Neural Network (MH-SA-DCNN) with Efficient Net Model. Brain cancer detection using MH-SA-DCNN with Efficient Net Model. This trains the algorithm to predict cancerous regions in brain images. Further, implement a Graph-Based Deep Neural Network Model (G-DNN) to capture spatial relationships and risk factors from brain images. Cox regression model to estimate cancer risk over time and fine-tune and optimize the model’s parameters and features using the Osprey optimization algorithm (OPA).

Unveiling Lung Diseases in CT Scan Images With a Hybrid Bio‐Inspired Mutated Spider‐Monkey and Crow Search Algorithm

ABSTRACTBio‐inspired computer‐aided diagnosis (CAD) has garnered significant attention in recent years due to the inherent advantages of bio‐inspired evolutionary algorithms (EAs) in handling small datasets with elevated precision and reduced computational complexity. Traditional CAD models face limitations as they can only be developed post‐outbreak, relying on datasets that become available during such events such as the COVID‐19 pandemic. The scarcity of data for emerging diseases poses a substantial challenge to achieving elevated precision with conventional deep‐learning algorithms. Furthermore, even when datasets are available, employing deep learning for class‐based classification is arduous, necessitating model retraining, in this paper, we propose a novel hybrid algorithm that leverages the strengths of the crow search algorithm (CSA) and the spider monkey optimization (SMO) algorithm to create an optimised spider monkey crow search (OSM‐CS) algorithm. We developed a CAD tool that maps each input CT image to a high‐dimensional vector by extracting four categories of features: high contrast, polynomial decomposition, textural, and pixel statistics. The proposed OSM‐CS algorithm is employed as a feature selection method. Our experimental results demonstrate the effectiveness of the OSM‐CS algorithm, achieving an impressive accuracy of 98.2% when coupled with an AdaBoost classifier for multi‐class classification and 99.93% for binary classification. This performance surpasses that of state‐of‐the‐art (SOTA) deep learning models and recently published hybrid algorithms, underscoring the potential of the OSM‐CS algorithm as a powerful tool in the realm of CAD.

Development and validity evidence of an interactive 3D model for thoracic and lumbar spinal fractures pedagogy: a first step of validity study.

Thoracic and lumbar spinal fractures are common in trauma care, requiring accurate classification to guide appropriate treatment. While traditional teaching methods use static 2D images, there is a growing need for interactive tools to improve understanding. This study addresses the lack of interactive three-dimensional (3D) models for teaching the AO (Arbeitsgemeinschaft für Osteosynthesefragen) Spine classification for thoracic and lumbar fractures. A free and open-access interactive 3D model of thoracic and lumbar spinal fractures was developed. The study aimed to provide preliminary validity evidence. We hypothesized that this model would be a valid educational tool for teaching the AO Spine classification, receiving high scores from senior spine surgeons on a validation questionnaire regarding anatomical realism and pedagogical value. The primary endpoint was the percentage of surgeons rating the model ≥ 8/10 on the Likert scale for content validation. We hypothesized that the 3D model would be validated by at least 75% of participating senior spine surgeons (rating ≥ 8/10) for anatomical realism and pedagogical value. The 3D model was created using the Blender® software, incorporating CT-scan images of a lumbar spine. AO Spine classification was used to recreate spinal fractures animations. The model could be used on any computer or smartphone, directly online. A total of 24 senior spine surgeons (5 professors, 6 fellows, 8 hospital practitioners, and 5 private practitioners) evaluated the 3D model using a structured questionnaire with seven Likert-scale items, assessing anatomical realism, fracture representation, adherence to the AO Spine classification, pedagogical value, and ease of use. A score of ≥ 8/10 was considered a positive validation. Group comparisons were made based on hospital activity and age. The 3D model was positively validated by 92% of surgeons for anatomical realism, 88% for fracture representation, and 92% for adherence to the AO Spine classification. The model's educational value for junior residents was rated positively by 100% of participants. Six out of 24 surgeons (25%) rated the ease of navigation < 8/10. Group comparisons revealed that university-affiliated surgeons rated the model higher overall (mean score 9.25/10) compared to private practitioners, who gave the lowest ratings (mean score 8.6/10). No significant correlation was found between age and ease of navigation (p = 0.948). The developed 3D model of thoracic and lumbar spine fractures is the first of its kind. It provides an innovative, open-access and freely online accessible tool for teaching the AO Spine classification. The findings demonstrate that it is a valid pedagogical tool for teaching the AO Spine classification, with strong support for its anatomical accuracy and pedagogical effectiveness. This study sets the stage for a future validation study with surgical residents. III.

CFD Analysis of the Choledynamic Flow Characteristics of a Patient with Gallbladder Carcinoma

Gallbladder carcinoma (GBC) is a rare malignancy with a low detection rate due to non-specific symptoms, complicating early diagnosis and treatment. This study aims to improve pre-operative and post-operative surgical analysis using patient-specific CFD analysis. CT scan images of a male patient with GBC were segmented using 3D Slicer software, and the biliary tree geometry was imported into ANSYS Workbench for fluid and solid meshing. The mechanical properties of the biliary tree and the rheological properties of bile were determined, with an LRN k-ω model used for simulating the gallbladder refilling stage. Results indicate that unhealthy bile flows slower than healthy bile, suggesting a link between obstructed bile flow and tumors or stones. Tumor-induced blockages cause pressure accumulation in the gallbladder neck, unlike the minimum pressure in healthy cases. These findings highlight the complex interplay between bile rheology, tumor development, and pressure dynamics, suggesting the model’s potential use in surgical planning.

Scar endometriosis, a form of abdominal wall endometriosis-a neglected obstetrical complication?

Scar endometriosis (EM) is defined by the presence of endometrial-like tissue outside the uterine cavity within the scar region after abdominal or pelvic surgery. It is a form of abdominal wall EM. This systematic review addresses the question of whether women after cesarean delivery (CD) are more frequently affected by scar EM than women after other pelvic surgical procedures. The primary aim is to analyze the distribution of previous operations in patients with scar EM. Secondarily, symptoms, diagnosis, and treatment are described. A systematic literature search in MEDLINE (Pubmed) was performed. Twelve studies were included. The terminology of scar EM is unspecific and the descriptions are, therefore, of limited comparability among authors. In 64-96%, patients with scar EM had a history of CD, followed by laparoscopy, laparotomy, and episiotomy. The main symptoms were pain, often cyclical, and the presence of local swelling. For diagnosis ultrasound, CT scan and MR imaging were used. All patients had undergone surgical resection and the diagnosis was confirmed. Most often scar EM develops after CD. Diagnosis and treatment are often delayed. As an objective classifications system is missing, we propose a simple objective descriptive tool for abdominal wall EM.

Unnecessary Medical Imaging and Determinant Factors in a District Hospital of Iran: A Cross-Sectional Study.

Due to the increasing use of imaging services, this study aimed to estimate the number of unnecessary imaging services, normal and abnormal imaging examinations, and their determinant factors. This cross-sectional study was conducted at a district hospital in East Azerbaijan, Iran. Retrospective data were collected from the electronic medical records of patients referred to the Imaging Department between March and August 2022, using the Hospital Information System (HIS). Descriptive statistics and χ 2 tests were performed, followed by hierarchical logistic regression with AOR, 95% CI, using SPSS-24 for analysis. About 28% of imaging examinations with abnormal results and 21% of the examinations with normal results were unnecessary. The probability of ultrasound abnormal imaging results was 67% lower than computed tomography (CT) scan (CT-scan) (adjusted odds ratio (AOR) = 0.33 (0.212-0.50); p < 0.001). One year increase of patient age is associated with a 1% lower likelihood of having normal imaging (AOR = 0.99 (0.98-0.99); p < 0/001). Women were 20% less likely than men to have abnormal imaging results (AOR = 0.80 (0.65-0.98); p = 0.035). The probability of necessary imaging decreases by 1% for 1 year increase in patient age (p = 0.017), on the other hand, the probability of unnecessary chest radiography was 5% higher than a brain CT-scan imaging (AOR = 2.05 (1.19-3.51); p = 0.009). Unnecessary imaging were more frequently performed on the older patients. Additionally, ultrasound was less likely than CT-scans to show abnormal findings, while chest radiography was more frequently deemed unnecessary compared to brain CT-scans. These insights highlight the need for age- and modality-specific guidelines to reduce unnecessary imaging and improve diagnostic efficiency.

COVID-19 chest CT scan image segmentation based on chaotic gravitational search algorithm

COVID-19 chest CT scan image segmentation based on chaotic gravitational search algorithm

A four in one nanoplatform: Theranostic bismuth-containing nanoMOFs for chemo-photodynamic- radiation therapy and CT scan imaging

Integration of different therapeutic performances into one platform is an innovative development for using multiple applications in real-time. In this paper, for the first time we exploited the concurrent capacity of radio and photosensitizing in a theranostic nanoMOFs based on bismuth, zirconium, and porphyrin. The porosity of nanoMOFs provided the capability of doxorubicin loading and chemotherapy besides enhanced photodynamic and radiation therapy (PDT & RT). Its PEGylation and aptamer (MUC1) immobilization endowed the platform with high biocompatibility and targeted tumor killing, respectively. In vitro assay exhibited that this aptamer immobilized DOX-loaded PEGylated MOF (Apt@DOX) produced more toxicity against 4 T1 cells compared to non-targeted nanoparticles (NP@DOX), especially when the treatment combined with PDT or/and RT. In vivo experiment also provided great results for tumor growth, survival rate, and body weight for 4 T1 bearing mice injected by Apt@DOX in combination with irradiation by 660 nm laser and/or exposure to 3 Gy dosage of X-ray radiation. The CT imaging of injected mice with targeted and non-targeted bismuth-based MOF introduced this nanoplatform as a promising CT contrast agent. Resultantly, we can present our as-synthesized nanoplatform as an efficient multifunctional theranostics with the ability of multimodal therapy and diagnostic performance.

Combination of Image Enhancement and Double U-Net Architecture for Liver Segmentation in CT-Scan Images

Liver cancer can be identified using CT-Scan liver image segmentation. Liver segmentation can be performed using CNN architecture like U-Net. However, the segmentation results using U-Net architecture are affected by image quality. Low image quality can affect the accuracy of segmentation results. This study proposes a combination of image enhancement and segmentation stages on CT-Scan liver images. Image enhancement is achieved by using a combination of CLAHE to enhance contrast and Bilateral Filter to reduce noise. The segmentation architecture proposed in this study is Double U-Net which is a development of U-Net architecture by adding a second U-Net block with the same structure as a single U-Net. The first U-Net is used to extract simple features, while the second U-Net is used to extract more complex features and enhance the segmentation results of the first U-Net. PSNR and SSIM measure the results of image enhancement. The PSNR is more than 40dB and the SSIM result is close to 1. These results show that the proposed image enhancement method can enhance the quality of original images. The segmentation results were measured by calculating accuracy, sensitivity, specificity, dice score, and IoU. The result of liver segmentation obtained 99% for accuracy, 98% for sensitivity, 99% for specificity, 98% for dice score, and 90% for IoU. This shows that liver segmentation using Double U-Net obtained good segmentation. Results of image enhancement and image segmentation show that the proposed method is very good for enhancing image quality and performing liver segmentation accurately.

Machine Learning Based Framework for Lung Cancer Detection and Image Feature Extraction Using VGG16 with PCA on CT-Scan Images

Machine Learning Based Framework for Lung Cancer Detection and Image Feature Extraction Using VGG16 with PCA on CT-Scan Images

Deep learning-based Covid-19 diagnosis: a thorough assessment with a focus on generalization capabilities

The Covid-19 pandemic has significantly spurred the development of deep learning (DL) models for the pathology automatic diagnosis based on CT scan images. However, the assumption about the generalization of the proposed models remains to be assessed and shown for concrete clinical use. In this work, we have investigated the real value of widely used public datasets for the elaboration of DL models that are dedicated to automatic diagnosis of Covid-19 using CT scans. We have collected various international public datasets from 13 countries. Different Convolutional Neural Networks (CNNs) have been trained and their performances carefully assessed. Two evaluations have been conducted: (1) an internal evaluation following a cross-validation procedure, and (2) an external evaluation on real patients coming from new and different sources. The objective is to assess the generalization capabilities considering real-world conditions: different acquisition conditions, devices and configurations. Three families from the most effective CNN models have been selected (ResNet, DenseNet and EfficientNet). These have been fine-tuned, evaluated and used within a training methodology based on transfer learning. The most effective models have been further customized in order to create new models that are dedicated to the task at hand. These models have significantly improved the diagnosis performance.

CTRing: An R package to extract wood density profiles from computed tomography images of discs and logs

Accurately determining the position of pith and accessing tree-ring density profiles, including intra-ring variations, is important for both the forest industry and dendroclimatology. Although several available methods exist for acquiring this information, such as X-ray computed tomography (CT), micro-CT, and X-ray films, the availability of open-source programs for extracting data remains limited. The CTRing package in the R environment integrates a series of functions to detect precisely the pith and tree-ring boundaries and generate tree-ring density profiles using CT images of tree cross sections. Before processing, grey values are transformed into density using a calibration function. Pith position is then detected by combining an adapted Hough Transform method and a one-dimensional edge detector. Tree-ring profiles along the pith-to-bark path of interest are inspected visually, and tree-ring boundaries can be easily added or removed manually via a graphical user interface. After correcting for tree-ring boundaries, the inflection points of a 3rd-degree polynomial obtained from density profiles are used to delimit the earlywood–latewood transition. We tested this package using 60 CT-scanned images of white spruce (Picea glauca (Moench) Voss) discs collected at various tree heights (0 %, 25 %, 50 % and 75 % of the total tree height as well as at 1.3 m). The pith detection function had an average mean error of 0.72 mm with 95 % of the automatically detected pith locations that differed by less than 2 mm from their manually located positions. Error decreased toward the apex of the tree. The functions of the CTRing package are flexible and can be easily implemented or adapted. The package could also be used with simple images of discs to obtain ring-width time series; however, this use must be evaluated further. Future work with this package involves assessing the use of low-quality images and ring-porous species.

Robot-assisted technique can achieve accurate screw placement in four-corner fusion and reduce operative difficulty: a cadaver study

BackgroundThe purpose of this study is to explore the feasibility and accuracy of a robot-assisted technique in four-corner fusion compared with traditional freehand operation.MethodsTwenty cadaver specimens were randomly assigned to the robot-assisted group and freehand groups. Three screws were placed percutaneously to fix the capitate-lunate joint, lunate-triquetrum joint, and triquetrum-hamate-capitate joint in each specimen by robot-assisted or freehand technique. The offset between the actual and planned screw positions was determined by merging the images of intraoperative and postoperative CT scans in the robot-assisted group. The centrality of the screw, time-consuming, drilling attempts, and radiation exposure were compared between the two groups.ResultsThe mean offset between the actual and planned screw position was 1.09 (SD: 0.56) mm. The offset at the start point of the screw was significantly lower than that at the endpoint. There was no significant difference in the centrality of the screws, surgical time between the two groups. The number of drilling attempts and the radiation dose received by surgeons were significantly lower in the robot-assisted group.ConclusionsAlthough there was no significant difference in screw centrality between the two groups, the slight offset between the actual and planned screw positions confirmed the feasibility of the robot-assisted technique in four-corner fusion. The robot-assisted technique has advantages in reducing the difficulty of surgery and protecting the surgeon from exposure to large doses of radiation.

Non-small cell lung cancer detection through knowledge distillation approach with teaching assistant.

Non-small cell lung cancer (NSCLC) exhibits a comparatively slower rate of metastasis in contrast to small cell lung cancer, contributing to approximately 85% of the global patient population. In this work, leveraging CT scan images, we deploy a knowledge distillation technique within teaching assistant (TA) and student frameworks for NSCLC classification. We employed various deep learning models, CNN, VGG19, ResNet152v2, Swin, CCT, and ViT, and assigned roles as teacher, teaching assistant and student. Evaluation underscores exceptional model performance in performance metrics achieved via cost-sensitive learning and precise hyperparameter (alpha and temperature) fine-tuning, highlighting the model's efficiency in lung cancer tumor prediction and classification. The applied TA (ResNet152) and student (CNN) models achieved 90.99% and 94.53% test accuracies, respectively, with optimal hyperparameters (alpha = 0.7 and temperature = 7). The implementation of the TA framework improves the overall performance of the student model. After obtaining Shapley values, explainable AI is applied with a partition explainer to check each class's contribution, further enhancing the transparency of the implemented deep learning techniques. Finally, a web application designed to make it user-friendly and classify lung types in recently captured images. The execution of the three-stage knowledge distillation technique proved efficient with significantly reduced trainable parameters and training time applicable for memory-constrained edge devices.

Evaluating process parameters of SLS part of polyamide-12 recycled powder using RSM

Abstract Selective laser sintering (SLS) is a highly flexible rapid prototyping (RP) technology which can use a wide range of powdered materials to generate high quality complex geometries directly from digital models. The SLS process uses a laser to sinter selected areas of powdered materials to produce solid objects layer by layer, according to the 2D data obtained from CT scan or MRI imaging techniques. This paper presents research work for evaluating the effect of process parameters on impact toughness and surface roughness of the part manufactured on Farsoon SS402P SLS from recycled ageing FS 3300PA which is PA-12 based powder and standard operating parameters using Response Surface Methodology (RSM) design of experiments. Impact tests have been performed as per the ASTM D6110 standard using recommendations from ISO 179-2. According to the results, effect of process parameters are successfully analyzed by employing impact toughness and surface roughness tests. Based on the results of the ANOVA analysis, impact toughness increases with increasing laser power (4.467 j/cm2 at 72.5 watts). Similarly, it is also clear from the results that the region with the lowest laser power, high layer thickness, and 90-degree orientation had the lowest surface roughness (3.46 μm).

Early-stage Alzheimer’s disease profiling in blood achieved by multiplexing aptamer-SERS biosensors

Neurological disorders are the second leading cause of death globally, with Alzheimer’s disease (AD) emerging as a significant contributor, responsible for 276 million cases in disability-adjusted life years. Conventional diagnostic methods are often invasive, costly, and place a considerable strain on global healthcare systems. In this study, we presented an innovative and efficient strategy for AD assessment through blood profiling using a multiwell glass chip integrated with aptamer-based surface-enhanced Raman scattering (SERS) biosensors. High-affinity aptamers were selected using capillary electrophoresis-based systematic evolution of ligands by exponential enrichment (CE-SELEX). A mouse brain injury model was employed to systematically investigate biomarkers indicative of physiological, vascular, and cellular damage, such as neurogranin (Nrgn), angiopoietin-2 (Angio-2), PRDX3, lactate dehydrogenase (L-LDH), and τ-441, which were quantified at atto-molar levels in blood samples. Additionally, with the aid of CT-scan imaging, an aptamer-SERS assay was developed to evaluate the dynamic regulation of AD biomarkers. The aptamer-SERS biosensor system was also applied to human samples, demonstrating its capability to multiplex AD biomarkers and establish a time-dependent correlation between percentage biomarker regulation and disease progression. The innovative design, fabrication of aptamer-SERS nanoprobes, and the bio-sensing outcomes illustrate the strong potential of this approach for selective, sensitive, and quantitative early-stage AD diagnosis in clinical applications.

Using Decision Tree With First and Second-Order Statistical Feature Extraction for Classification of Lung Cancer

&lt;p&gt;The classification of CT-Scan images on images with lung cancer and normal lung has been done by improving the image quality of the median and Gabor filters, extraction of first and second-order statistical features, and decision tree classification. The data used comes from LIDC-IDRI as much as 100 training data and 40 test data. The median filter removes noise without removing edges in the image. A Gabor filter is used to facilitate texture analysis on the image. At the feature extraction stage, statistical variations of the first order, second order statistics and the merging of first and second-order statistics. The best results obtained at the testing stage are program designs with variations of feature extraction combining first and second-order statistics. The level of accuracy obtained is 97.5%, with a sensitivity of 100% and a specificity of 95%.&lt;/p&gt;

Enhancing the Segmentation Accuracy of the Otsu Method for Calculating Blood Vessel Rupture Volumes in CT-Scan Images

The precise quantification of cerebral hemorrhage volume assumes paramount significance, given its consequential impact on subsequent medical interventions. While antecedent scholarly endeavors have undertaken cerebral hemorrhage volume computations, the attendant accuracy remains amenable to refinement. The main contribution to this research is the development of a rectangle algorithm using the Otsu method, which can be used to calculate the total volume of brain hemorrhage in CT-Scan images of the human brain. The present investigation seeks to augment the segmentation paradigm inherent to the Otsu method, thereby advancing the precision of cerebral hemorrhage volume assessments. The Otsu method is conceived with the purpose of automating the segmentation of the hemorrhagic domain through the determination of threshold values. The refinement of the Otsu method’s accuracy is effectuated by the formulation of an algorithm aimed at maximizing the mean discrepancy between object and background intensities within the object image, predicated upon the ascertained threshold values. These determined thresholds subsequently govern the transformation of grayscale images into binary format, achieved through the quotient of the threshold value and the mean variance, thereby amplifying segmentation precision. The incorporation of variance and mean values in the binarization of grayscale images affords adaptability in threshold modulation commensurate with the intensity spectrum of the image. The research corpus leverages CT-Scan images sourced from RSUP M. Djamil Padang, West Sumatra, encompassing a compendium of 100 image slices culled from a quintet of patients. Empirical findings attest to the fortification of the Otsu method in the segmentation milieu, substantiating the augmentation of threshold value accuracy. This enhancement is conspicuously manifest in the outcomes of segmentation tests, underscoring the superior efficacy of the Otsu method in discerning between objects and backgrounds. The corollary effect is a discernibly more accurate computation of volumes employing the Otsu-T methodology. The import of this research rests in its successful establishment of automated threshold values and the concomitant elevation of segmentation precision.