CT diagnosis of early stroke : the initial approach to the new CAD tool based on multiscale estimation of ischemia

Background: Computer aided diagnosis (CAD) becomes one of the most important diagnostic tools for urgent states in cerebral stroke and other life-threatening conditions where time plays a crucial role. Routine CT is still diagnostically insufficient in hyperacute stage of stroke that is in the therapeutic window for thrombolytic therapy. Authors present computer assistant of early ischemic stroke diagnosis that supports the radiologic interpretations. A new semantic-visualization system of ischemic symptoms applied to noncontrast, routine CT examination was based on multiscale image processing and diagnostic content estimation. Material/Methods: Evaluation of 95 sets of examinations in patients admitted to a hospital with symptoms suggesting stroke was undertaken by four radiologists from two medical centers unaware of the final clinical findings. All of the consecutive cases were considered as having no CT direct signs of hyperacute ischemia. At the first test stage only the CTs performed at the admission were evaluated independently by radiologists. Next, the same early scans were evaluated again with additional use of multiscale computer-assistant of stroke (MulCAS). Computerized suggestion with increased sensitivity to the subtle image manifestations of cerebral ischemia was constructed as additional view representing estimated diagnostic content with enhanced stroke symptoms synchronized to routine CT data preview. Follow-up CT examinations and clinical features confirmed or excluded the diagnosis of stroke constituting 'gold standard' to verify stroke detection performance. Results: Higher AUC (area under curve) values were found for MulCAS -aided radiological diagnosis for all readers and the differences were statistically significant for random readers-random cases parametric and non-parametric DBM MRMC analysis. Sensitivity and specificity of acute stroke detection for the readers was increased by 30% and 4%, respectively. Conclusions: Routine CT completed with proposed method of computer assisted diagnosis provided noticeable better diagnosis efficiency of acute stroke according to the rates and opinions of all test readers. Further research includes fully automatic detection of hypodense regions to complete assisted indications and formulate the suggestions of stroke cases more objectively. Planned prospective studies will let evaluate more accurately the impact of this CAD tool on diagnosis and further treatment in patients suffered from stroke. It is necessary to determine whether this method is possible to be applied widely

Development of High-Precision Three-Dimensional Images for Colonoscopy

Background: For three-dimensional image analysis, SYNAPSE VINCENT (Fujifilm, Japan) has been applied. Authors have continued various clinical practice and research with image study by computer analysis. Patient and Method: The patient is a 65-year-old female with further evaluation of colon. Method included abdominal computed tomography (CT) scan associated with injecting necessary amount of air from the anus. With maintaining this condition, CT scan was conducted two times of supine position and prone position. Results: The case had sigmoid cancer, which was presented irregular surface texture. Further, she had colon polyp in the sigmoid colon and diverticula in the descending colon. These images were presented in the display associated with i) the position and direction for observation, ii) CT image of the corresponding point, iii) endoscopic image for the corresponding region. Discussion: This image analysis system would be useful for clinical diagnosis, because the images have multiple angles and related situations of colon mucosa, folds, and stiffness and so on. The combined data can provide significant information including coronal, horizontal and sagittal planes. When the surgeon operates colon cancer, he can make the correct imagination of the lesion in advance. It seems to be beneficial for smooth treatment of surgical operation

Clearer Image of Computed Tomography by Reconstruction Analysis Method

Computed tomography (CT) has been widely used for long. In recently years, the useful application of CT would be the method of the reconstruction images. Historically speaking, reconstruction images were conducted so far by algebraic reconstruction technique (ART), filtered back projection (FBP), iterative reconstruction (IR) algorithms and so on. There are various effects of reconstruction image of CT in clinical practice. Two cases with such beneficial experiences were reported. Case 1 is 74-year-man with hypertension. He was diagnosed to have abdominal aortic aneurysm (AAA). By reconstruction images, the general shape and details were clearly observed including regulating background data. Case 2 is 48-year-female without history of low back pain. When she received abdominal CT, she was pointed out to have lumbar disc herniation between L4 and L5. The diagnosis was done from the lateral tomography of lumbar vertebrae. By reconstruction images with colored modification, the detail status of herniation was clearly observed. Consequently, reconstruction image method of CT would be useful and effective. Further evolution of CT apparatus and development of computer analysis would be expected for better clinical practice and research in the future

Improving Kidney Tumor Detection Accuracy Using Hybrid U-Net Segmentation

Kidney cancer stands as a significant factor in cancer-related mortality, highlighting the critical importance of early and precise tumor detection This study introduces a computer-aided approach using the KiTS19 dataset and a hybrid U-Net architecture. Manual tumor segmentation is resource-intensive and prone to errors. Leveraging the hybrid U-Net, known for its proficiency in medical image analysis, we achieve precise tumor identification. Our method involves initial kidney and tumor segmentation in high-resolution CT images, followed by region of interest (ROI) generation and benign/malignant tumor classification. The assessment conducted on the KiTS19 dataset demonstrates encouraging outcomes, with Dice coefficients of 0.974 for kidney segmentation and 0.818 for tumor segmentation, accompanied by a tumor classification accuracy rate of 94.3%.The hybrid U-Net’s advanced feature extraction and spatial context awareness contribute to these outcomes. By streamlining diagnosis, our approach has the potential to significantly improve patient outcomes. The use of the KiTS19 dataset ensures robustness across various clinical cases and imaging modalities. This method represents a valuable advancement in computer-aided kidney tumor detection, promising to enhance patient care

A region-based algorithm for automatic bone segmentation in volumetric CT

In Computed Tomography (CT), bone segmentation is considered an important step to extract bone parameters, which are frequently useful for computer-aided diagnosis, surgery and treatment of many diseases such as osteoporosis. Consequently, the development of accurate and reliable segmentation techniques is essential, since it often provides a great impact on quantitative image analysis and diagnosis outcome. This chapter presents an automated multistep approach for bone segmentation in volumetric CT datasets. It starts with a three-dimensional (3D) watershed operation on an image gradient magnitude. The outcome of the watershed algorithm is an over-partioning image of many 3D regions that can be merged, yielding a meaningful image partitioning. In order to reduce the number of regions, a merging procedure was performed that merges neighbouring regions presenting a mean intensity distribution difference of ±15%. Finally, once all bones have been distinguished in high contrast, the final 3D bone segmentation was achieved by selecting all regions with bone fragments, using the information retrieved by a threshold mask. The bones contours were accurately defined according to the watershed regions outlines instead of considering the thresholding segmentation result. This new method was tested to segment the rib cage on 185 CT images, acquired at the São João Hospital of Porto (Portugal) and evaluated using the dice similarity coefficient as a statistical validation metric, leading to a coefficient mean score of 0.89. This could represent a step forward towards accurate and automatic quantitative analysis in clinical environments and decreasing time-consumption, user dependence and subjectivity.The authors acknowledge to Foundation for Science and Technology (FCT) - Portugal for the fellowships with the references: SFRH/BD/74276/2010; SFRH/BD/68270/2010; and, SFRH/BPD/46851/2008. This work was also supported by FCT R&D project PTDC/SAU-BEB/103368/2008

Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519