GPU acceleration of liver enhancement for tumor segmentation

Most of abdominal CT images include Gaussian noise, and CT scans form a blurry vision because of the internal fat tissue inside of abdomen. These two handicaps (noise and fat tissue) constitute an impediment in front of an accurate abdominal organ & tumour segmentation. Also segmentation techniques generally fall into error on segmentation of close grayscale regions. Therefore, denoising and enhancement parts are crucial for better segmentation results on CT images. In this paper, we form a tool including three efficient algorithms for the purpose of image enhancement before abdominal organ & tumour segmentation. At first, the denoising process is realized by Block Matching and 3D Filtering (BM3D) algorithm for elimination of Gaussian noise stated in arterial phase CT images. At second, Fast Linking Spiking Cortical Model (FL-SCM) is used for removing the internal fat tissue. At last, Otsu algorithm is processed to remove the redundant parts within the image. In experiments, Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index are used to evaluate the performance of proposed method, and a visual comparison is presented. According to results, it is seen that proposed tool obtains the best PSNR and SSIM values in comparison with two steps of pipeline (FL-SCM and BM3D & FL-SCM). Consequently, BM3D & FL-SCM & Otsu (BFO) ensures a clean abdomen particularly for segmentation of liver, spleen, pancreas, adrenal tumours, aorta, ribs, spinal cord and kidneys.

Adaptive histogram equalization (AHE) is a method for adaptive contrast enhancement of digital images. It is an automatic, reproducible method for the simultaneous viewing of contrast within a digital image with a large dynamic range. Recent experiments have shown that in specific cases, there is no significant difference in the ability of AHE and linear intensity windowing to display gray-scale contrast. More recently, a variant of AHE which limits the allowed contrast enhancement of the image has been proposed. This contrast-limited adaptive histogram equalization (CLAHE) produces images in which the noise content of an image is not excessively enhanced, but in which sufficient contrast is provided for the visualization of structures within the image. Images processed with CLAHE have a more natural appearance and facilitate the comparison of different areas of an image. However, the reduced contrast enhancement of CLAHE may hinder the ability of an observer to detect the presence of some significant gray-scale contrast. In this report, a psychophysical observer experiment was performed to determine if there is a significant difference in the ability of AHE and CLAHE to depict gray-scale contrast. Observers were presented with computed tomography (CT) images of the chest processed with AHE and CLAHE. Subtle artificial lesions were introduced into some images. The observers were asked to rate their confidence regarding the presence of the lesions; this rating-scale data was analyzed using receiver operating characteristic (ROC) curve techniques. These ROC curves were compared for significant differences in the observers' performances. In this report, no difference was found in the abilities of AHE and CLAHE to depict contrast information.

Segmentation of liver tumors from Computed Tomography (CT) and tumor burden analysis play an important role in the choice of therapeutic strategies for liver diseases and treatment monitoring. In this paper, a new segmentation method for liver tumors from contrast-enhanced CT imaging is proposed. As manual segmentation of tumors for liver treatment planning is both labor intensive and time-consuming, a highly accurate automatic tumor segmentation is desired. The proposed framework is fully automatic requiring no user interaction. The proposed segmentation evaluated on real-world clinical data from patients is based on a hybrid method integrating cuckoo optimization and fuzzy c-means algorithm with random walkers algorithm. The accuracy of the proposed method was validated using a clinical liver dataset containing one of the highest numbers of tumors utilized for liver tumor segmentation containing 127 tumors in total with further validation of the results by a consultant radiologist. The proposed method was able to achieve one of the highest accuracies reported in the literature for liver tumor segmentation compared to other segmentation methods with a mean overlap error of 22.78 % and dice similarity coefficient of 0.75 in 3Dircadb dataset and a mean overlap error of 15.61 % and dice similarity coefficient of 0.81 in MIDAS dataset. The proposed method was able to outperform most other tumor segmentation methods reported in the literature while representing an overlap error improvement of 6 % compared to one of the best performing automatic methods in the literature. The proposed framework was able to provide consistently accurate results considering the number of tumors and the variations in tumor contrast enhancements and tumor appearances while the tumor burden was estimated with a mean error of 0.84 % in 3Dircadb dataset.

Small hepatocellular carcinomas in cirrhosis: differences in contrast enhancement effects between helical CT and MR imaging during multiphasic dynamic imaging

Introduction: Precise segmentation of the liver from computed tomography (CT) scans is an essential component in the diagnosis and treatment of liver diseases such as hepatocellular carcinoma. Manual segmentation can be time-consuming and subject to variability, spurring the application of automated systems based on deep learning. We introduce a 3D U-Net-based convolutional neural network for end-to-end segmentation of the liver from volumetric CT images. The model is trained and tested on the LiTS dataset using a standard preprocessing pipeline and an in-house composite loss function—AdvancedComboLoss—combining Dice Loss, Binary Cross-Entropy, and Focal Tversky Loss to balance class imbalance and boundary accuracy. The network handles entire 3D volumes with spatial context retained between anatomical planes and is trained on a GPU-enabled setup with the PyTorch environment. Objectives: To develop a highly accurate and computationally efficient 3D U-Net-based deep learning model for automated liver segmentation from volumetric CT scans, addressing challenges like class imbalance, anatomical variability, and low tissue contrast, using a custom loss function (AdvancedComboLoss) and a standardized preprocessing pipeline. Methods: The methodology adopted is volumetric liver segmentation from a specially designed 3D U-Net architecture specific to medical CT scans. The process entails preprocessing of raw CT scans into a common shape and intensity range and training a fully convolutional 3D neural network that learns the spatial relationships among all three anatomical planes. The model uses an encoder-decoder architecture with skip connections to maintain fine-grained structural information, allowing for accurate delineation of liver regions. A hybrid loss function—integrating Dice, Binary Cross-Entropy, and Focal Tversky losses—is used to direct the learning process to optimize overlap of regions, accuracy of classification, and class imbalance. Results: Experimental outcomes demonstrate that the model has a Dice score of 0.9604 and voxel-wise accuracy of 0.9979, indicating excellent performance in segmentation as well as strong agreement with expert annotations. The model presented in this paper presents a solid, scalable, and clinically valuable approach to automated liver segmentation with future potential to integrate into multi-stage liver and tumor analysis workflows. Conclusions: Herein, we built and tested a 3D U-Net deep learning architecture for unsupervised liver segmentation from abdominal CT volumes. Through utilization of volumetric convolutions as well as through a well-engineered composite loss function, the model was successful in capturing complicated liver anatomy as well as being able to hold high performance even on varied CT volumes. The power of the method is not just in its quantitative performance but also in its capability to generate anatomically consistent and visually plausible segmentations, making it extremely relevant to actual clinical workflows. Although there are some limitations—constrained batch size and absence of data augmentation—there is room for improvement, the findings affirm that deep learning can be an effective tool for automating key tasks in medical image analysis. As a whole, this paper adds a robust and effective segmentation model that would be potentially embedded in wider diagnostic workflows, ending up improving the accuracy, speed, and scalability of liver evaluation in clinical practice. In brief, this paper adds a high-fidelity, scalable, and computationally effective solution for computer-aided liver segmentation, pushing the application of artificial intelligence in clinical decision-making and medical image analysis.

Contrast enhancement efficacy of iodinated contrast media: Effect of molecular structure on contrast enhancement

Medical image processing and analysis operations, particularly segmentation, can benefit a great deal from prior information encoded to capture variations over a population in form, shape, anatomic layout, and image appearance of objects. Model/atlas-based methods are extant in medical image segmentation. Although multi-atlas/ multi-model methods have shown improved accuracy for image segmentation, if the atlases/models do not cover representatively the distinct groups, then the methods may not be generalizable to new populations. In a previous study, we have given an answer to address the following problem at image level: How many models/ atlases are needed for optimally encoding prior information to address the differing body habitus factor in a population? However, the number of models for different objects may be different, and at the image level, it may not be possible to infer the number of models needed for each object. So, the modified question to which we are now seeking an answer to in this paper is: How many models/ atlases are needed for optimally encoding prior information to address the differing body habitus factor for each object in a body region? To answer this question, we modified our method in the previous study for seeking the optimum grouping for a given population of images but focusing on the individual objects. We present our results on head and neck computed tomography (CT) scans of 298 patients.

7 - A survey on neutrosophic medical image segmentation

Iodinated contrast agent is frequently used in computed tomography (CT) imaging to enhance organ contrast enhancement and improve diagnostic sensitivity. Despite this importance, there currently is a lack of standardization in contrast administration protocol across institutions, leading to many safety and clinical diagnostic risks. To solve this, we built three liver contrast enhancement/perfusion models: two using simple linear regression and another by combining a pre-existing pharmacokinetics mathematical model with clinical data with the eventual goal of individualizing contrast administration protocol to optimize contrast-enhanced CT imaging for each patient. These models primarily use patient attributes, such as height, weight, sex, age and contrast administration information, and bolus tracking information to make such predictions. 418 Chest/Abdomen/Pelvis CT scans were used in this study. 75% of cases were used to train these models and the rest were used to test the prediction accuracy. Pearson’s correlation coefficient test was used to find the correlations between the patient attributes and contrast enhancement in liver parenchyma. Weight, height, BMI, and lean body mass were found to be statistically significant predictors for contrast enhancement (P<0.05), with weight as the strongest predictor. Of the predictive models, we found that including bolus tracking information increases predictive accuracy (r2=0.75 v. 0.42) and that in the absence of bolus tracking information, combining clinical data with pre-existing pharmacokinetics model may provide the needed enhancement curve.

The objects in the medical images are not visible due to low contrast and the noise. In general, X-ray, computed tomography (CT), and magnetic resonance imaging (MRI) images are often affected by blurriness, lack of contrast, which are very important for the accuracy of medical diagnosis. It is difficult to segmentation in such case without losing the details of the objects. The goal of image enhancement is to improve certain details of an image and to improve its visual quality. So, image enhancement technology is one of the key procedures in image segmentation for medical imaging. This article presents a two-stage approach, combining novel and traditional algorithms, for the enhancement and segmentation of images of bones obtained from CT. The first stage is a new combined local and global transform domain-based image enhancement algorithm. The basic idea of using local alfa-rooting method is to apply it to different disjoint blocks of different sizes. We used image enhancement non-reference quality measure for optimization alfa-rooting parameters. The second stage applies the modified active contour method based on an anisotropic gradient. The simulation results of the proposed algorithm are compared with other state-of-the-art segmentation methods, and its superiority in the presence of noise and blurred edges on the database of CT images is illustrated.

Despite recent advances in contrast enhancement, it remains difficult for existing methods to simultaneously achieve consistent improvements in image brightness and contrast in both low-light and normal-light images. To address this issue, we revisit 2D histogram equalization methods and extend them to accommodate more diversified and poor lighting conditions. The extension is based on the observation that the contrast needs to be improved by increasing the intensity difference between spatially neighboring pixels, while the degree of increase should be proportional to the difference in their reflectances. On the basis of this observation, we propose to embed the inter-pixel contextual information of image reflectance into the 2D histogram of intensity co-occurrence. An intensity mapping function can thus be derived by solving optimization problems formulated with the 2D histogram, leading to two novel contrast enhancement methods. Qualitative and quantitative evaluations on more than 600 images showed that the proposed methods are superior to state-of-the-art contrast enhancement methods. It was also shown that the reflectance of an image provides important visual information on the significance and objectness of local image areas. Using this reflectance as a clue, the degree of contrast enhancement can be adaptively derived to achieve sufficient brightness improvement in low-light images and to avoid excessive enhancement in normal-light images.

CT and MRI studies of hepatic angiosarcoma

Liver tumour segmentation from abdominal CT image is a challenging task in biomedical image processing. Complex segmentation methods may provide good segmentation results but are difficult for implementing in clinical environment. Hence, in this paper, we propose a simple thresholding technique based on fuzzy logic and Shannon's entropy function to segment the tumour from liver CT images. Tumour dimensions are then measured subsequently. The proposed method provides better results than the classical segmentation methods.

CT, MRI, and FDG-PET/CT imaging findings of abdominopelvic desmoplastic small round cell tumors: Correlation with histopathologic findings

ObjectiveTo study the feasibility and the potential benefits of defining the internal gross tumor volume (IGTV) of hepatocellular carcinoma (HCC) using contrast-enhanced 4D CT images obtained by combining arterial-phase (AP) contrast-enhanced (CE) 3D CT and non-contrast-enhanced (NCE) 4D CT images using deformable registration (DR).MethodsTen HCC patients who had received radiotherapy beforehand were selected for this study. The following CT simulation images were acquired sequentially: NCE 4D CT in free breathing, NCE 3D CT and APCE 3D CT in end-expiration breath holding. All 4D CT images were sorted into ten phases according to breath cycle (CT00 ~ CT90). Gross tumor volumes (GTVs) were contoured on all CT images and the IGTV-1 was obtained by merging the GTVs in each phase of 4D CT images. The GTV on the APCE 3D CT image was deformably registered to each 4D CT phase image according to liver shape using RayStationTM 3.99.0.7 version treatment planning system. The IGTV-DR was obtained by merging the GTVs after DR on the 4D CT images. Volume differences among the GTVs and between the IGTV-1 and the IGTV-DR were compared.ResultsThe edge of most lesions could be definitively identified using APCE 3D CT images compared to NCE 4D and 3D CT images. The GTV volume on APCE 3D CT images increased by an average of 34.79% (P < 0.05). There was no significant difference among the GTV volumes obtained using NCE 4D and 3D CT images (P > 0.05). The GTV volumes after DR on 4D CT different phase images increased by an average of 36.29% (P < 0.05), as was observed using the APCE 3D CT image (P > 0.05). Lastly, the volume of IGTV-DR increased by an average of 19.91% compared to that of IGTV-1 (P < 0.05).ConclusionNCE 4D CT imaging alone has the potential risk of missing a partial volume of the HCC. The combination of APCE 3D CT and NCE 4D CT images using the DR technique improved the accuracy of the definition of the IGTV in HCC.