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The human-brain is a vital and complicated organ within the body. Identifying brain-related diseases can be challenging. Typically, Magnetic Resonance Imaging (MRI) scanning methods are used to gain insights of the protected regions in the body. Brain segmentation can result in identifying region boundaries as a set of contours. However, segmenting brain images poses several challenges, including noise, bias field, and partial volume effect (PVE). Removing noise, accurately segmenting tissues and tumors are crucial for effective evaluation. To enhance tissue and tumor segmentation, a new machine learning-based method called as Gaussian-Kernelized Enhanced Intuitionistic Fuzzy-C-Means (GKEIFCM) has been proposed. Approach enhances Improved Intuitionistic Fuzzy-C-Means Algorithm (IIFCM) by utilizing Gaussian kernelized distance between pixels, resulting in uncomplicated segmentation with reduced computational times and improved efficiency. This proposed novel method proved to be expertise in tissue and tumor classification and identification respectively. The results demonstrate the effectiveness of GKEIFCM interms of Dice, Jaccard-similarity-index, Accuracy and Execution time. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is one of the most common malignant tumors in women. A noninvasive ultrasound examination can identify mammary-gland-related diseases and is well tolerated by dense breast, making it a preferred method for breast cancer screening and of significant clinical value. However, the diagnosis of breast nodules or masses via ultrasound is performed by a doctor in real time, which is time-consuming and subjective. Junior doctors are prone to missed diagnoses, especially in remote areas or grass-roots hospitals, due to limited medical resources and other factors, which bring great risks to a patient's health. Therefore, there is an urgent need to develop fast and accurate ultrasound image analysis algorithms to assist diagnoses. Methods: We propose a breast ultrasound image-based assisted-diagnosis method based on convolutional neural networks, which can effectively improve the diagnostic speed and the early screening rate of breast cancer. Our method consists of two stages: tumor recognition and tumor classification. (1) Attention-based semantic segmentation is used to identify the location and size of the tumor; (2) the identified nodules are cropped to construct a training dataset. Then, a convolutional neural network for the diagnosis of benign and malignant breast nodules is trained on this dataset. We collected 2057 images from 1131 patients as the training and validation dataset, and 100 images of the patients with accurate pathological criteria were used as the test dataset. Results: The experimental results based on this dataset show that the MIoU of tumor location recognition is 0.89 and the average accuracy of benign and malignant diagnoses is 97%. The diagnosis performance of the developed diagnostic system is basically consistent with that of senior doctors and is superior to that of junior doctors. In addition, we can provide the doctor with a preliminary diagnosis so that it can be diagnosed quickly. Conclusion: Our proposed method can effectively improve diagnostic speed and the early screening rate of breast cancer. The system provides a valuable aid for the ultrasonic diagnosis of breast cancer. [ABSTRACT FROM AUTHOR]

Background: The Regional Basis of Solid Tumor (RBST), a clinical data warehouse, centralizes information related to cancer patient care in 5 health establishments in 2 French departments. Purpose: To develop algorithms matching heterogeneous data to "real" patients and "real" tumors with respect to patient identification (PI) and tumor identification (TI). Methods: A graph database programed in java Neo4j was used to build the RBST with data from ~20 000 patients. The PI algorithm using the Levenshtein distance was based on the regulatory criteria identifying a patient. A TI algorithm was built on 6 characteristics: tumor location and laterality, date of diagnosis, histology, primary and metastatic status. Given the heterogeneous nature and semantics of the collected data, the creation of repositories (organ, synonym, and histology repositories) was required. The TI algorithm used the Dice coefficient to match tumors. Results: Patients matched if there was complete agreement of the given name, surname, sex, and date/month/year of birth. These parameters were assigned weights of 28%, 28%, 21%, and 23% (with 18% for year, 2.5% for month, and 2.5% for day), respectively. The algorithm had a sensitivity of 99.69% (95% confidence interval [CI] [98.89%, 99.96%]) and a specificity of 100% (95% CI [99.72%, 100%]). The TI algorithm used repositories, weights were assigned to the diagnosis date and associated organ (37.5% and 37.5%, respectively), laterality (16%) histology (5%), and metastatic status (4%). This algorithm had a sensitivity of 71% (95% CI [62.68%, 78.25%]) and a specificity of 100% (95% CI [94.31%, 100%]). Conclusion: The RBST encompasses 2 quality controls: PI and TI. It facilitates the implementation of transversal structuring and assessments of the performance of the provided care. [ABSTRACT FROM AUTHOR]

Pathology analysis is crucial to precise cancer diagnoses and the succeeding treatment plan as well. To detect abnormality in histopathology images with prevailing patch-based convolutional neural networks (CNNs), contextual information often serves as a powerful cue. However, as whole-slide images (WSIs) are characterized by intense morphological heterogeneity and extensive tissue scale, a straightforward visual span to a larger context may not well capture the information closely associated with the focal patch. In this paper, we propose a novel pixel-offset based patch-location method to identify high-representative tissues, with a CNN backbone. Pathology Deformable Conditional Random Field (PDCRF) is proposed to learn the offsets and weights of neighboring contexts in a spatial-adaptive manner, to search for high-representative patches. A CNN structure with the localized patches as training input is then capable of consistently reaching superior classification outcomes for histology images. Overall, the proposed method has achieved state-of-the-art performance, in terms of the test classification accuracy improvement to the baseline by 1.15-2.60%, 0.78-1.78%, and 1.47-2.18% on TCGA public datasets of TCGA-STAD, TCGA-COAD, and TCGA-READ respectively. It also achieves 88.95% test accuracy and 0.920 test AUC on Camelyon 16. To show the effectiveness of the proposed framework on downstream tasks, we take a further step by incorporating an active learning model, which noticeably reduces the number of manual annotations by PDCRF to reach a parallel patch-based histology classifier. [ABSTRACT FROM AUTHOR]

Malignant tumors have always been the biggest problem facing human survival, and a huge number of people die from cancer every year. Therefore, the identification and detection of malignant tumors have far-reaching significance for human survival and development. Some substances are abnormally expressed in tumors, such as cyclooxygenase-2 (COX-2), nitroreductase (NTR), pH, biothiols (GSH, Cys, Hcy), hydrogen sulfide (H2S), hydrogen sulfide (H2O2), hypochlorous acid (HOCl) and NADH. Consequently, it is of great value to diagnose and treat malignant tumors due to the identification and detection of these substances. Compared with traditional tumor detection methods, fluorescence imaging technology has the advantages of an inexpensive cost, fast detection and high sensitivity. Herein, we mainly introduce the research progress of fluorescent probes for identifying and detecting abnormally expressed substances in several tumors. [ABSTRACT FROM AUTHOR]

The early automated identification of brain tumors is a difficult task in MRI images. For a long time, continuous research efforts have floated a new idea of replacing different grayscale anatomic regions of diagnostic images with appropriate colors that could overcome the problems being faced by radiologists. The colorization of grayscale images is challenging for enhancing various regions' contrasts by transforming grayscale images into high-contrast color images. This study investigates standard solutions in discriminating between normal and abnormal regions by assigning colors to grayscale human brain MR images to differentiate different kinds of tissues. The proposed approach is influenced by connected component and index-based colorization methods for applying colors to different regions and abnormal areas. It is an automated approach that varies its inputs using luminance and pixel matrix values and provides the possible outcome. After segmentation, a specific algorithm is devised to colorize the region-of-interest (ROI) areas, which distinguishes and applies colors to differentiate the regions. Results show that implementing the watershed-based area segmentation method and ROI selection method based on the morphological operation helps identify tissues during processing. Moreover, the colorization approach based on luminance and pixel matrix after segmentation and ROI selection is beneficial due to better PSNR and SSIM values and visible contrast improvement. Our proposed algorithm works with less processing overhead and uses less time than those of the industry's previously used color transfer method. [ABSTRACT FROM AUTHOR]

Inaccuracies in anomaly prediction have become an alarming issue in the field of medical image analysis, and these quandaries have burgeoned due to the errors caused by the operator, instrument/device and environment, whereof, these troubles can be unanimously rectified with the advent of a novel segmentation approach proposed through this article. The novel approach encapsulates the functionary of spatially constrained fish school optimization (SCFSO) algorithm and interval type-II fuzzy logic system (IT2FLS) techniques, which resolves the erroneous prediction of anomalies present in various topographical locations in brain subjects of magnetic resonance imaging (MRI) modality. Huge datasets and complex tumor (anomalies) can be intervened and examined with ease by the developed approach, and this could be a proactive measure for being implemented or incorporated in clinical practice for the betterment of both doctors and patients, and it can perpetuate a profound experience to the doctors. The developed SCFSO-IT2FLS technique was applied to BRATS-SICAS dataset, and the evaluation metrics—namely, the Dice overlap index and sensitivity value were delivered as 96 ± 2.1 and 98 ± 1.1 by the proposed technique. These values are better than the conventional techniques and the proposed technique is applicable for the segmentation of T1-weighted, T2-weighted, and fluid attenuated inversion recovery MRI sequences of various axes coordination. A clear extraction of tumor region from nontumor region (edema) is rendered by the proposed technique and a therapeutic preplanning could always be made with such a provision/advantage. [ABSTRACT FROM AUTHOR]

Malignant tumors have always been the biggest problem facing human survival, and a huge number of people die from cancer every year. Therefore, the identification and detection of malignant tumors have far-reaching significance for human survival and development. Some substances are abnormally expressed in tumors, such as cyclooxygenase-2 (COX-2), nitroreductase (NTR), pH, biothiols (GSH, Cys, Hcy), hydrogen sulfide (H2S), hydrogen sulfide (H2O2), hypochlorous acid (HOCl) and NADH. Consequently, it is of great value to diagnose and treat malignant tumors due to the identification and detection of these substances. Compared with traditional tumor detection methods, fluorescence imaging technology has the advantages of an inexpensive cost, fast detection and high sensitivity. Herein, we mainly introduce the research progress of fluorescent probes for identifying and detecting abnormally expressed substances in several tumors.

The early automated identification of brain tumors is a difficult task in MRI images. For a long time, continuous research efforts have floated a new idea of replacing different grayscale anatomic regions of diagnostic images with appropriate colors that could overcome the problems being faced by radiologists. The colorization of grayscale images is challenging for enhancing various regions’ contrasts by transforming grayscale images into high-contrast color images. This study investigates standard solutions in discriminating between normal and abnormal regions by assigning colors to grayscale human brain MR images to differentiate different kinds of tissues. The proposed approach is influenced by connected component and index-based colorization methods for applying colors to different regions and abnormal areas. It is an automated approach that varies its inputs using luminance and pixel matrix values and provides the possible outcome. After segmentation, a specific algorithm is devised to colorize the region-of-interest (ROI) areas, which distinguishes and applies colors to differentiate the regions. Results show that implementing the watershed-based area segmentation method and ROI selection method based on the morphological operation helps identify tissues during processing. Moreover, the colorization approach based on luminance and pixel matrix after segmentation and ROI selection is beneficial due to better PSNR and SSIM values and visible contrast improvement. Our proposed algorithm works with less processing overhead and uses less time than those of the industry’s previously used color transfer method.

Identification and characterization of nodules in soft tissue, including their size, shape, and location, provide a basis for tumor identification. This study proposes an inverse finite‐element (FE) based computational framework, for characterizing the size of examined tissue sample and detecting the presence of embedded tumor nodules using instrumented palpation, without a priori anatomical knowledge. The inverse analysis was applied to a model system, the human prostate, and was based on the reaction forces which can be obtained by trans‐rectal mechanical probing and those from an equivalent FE model, which was optimized iteratively, by minimizing an error function between the two cases, toward the target solution. The tumor nodule can be identified through its influence on the stress state of the prostate. The effectiveness of the proposed method was further verified using a realistic prostate model reconstructed from magnetic resonance (MR) images. The results show the proposed framework to be capable of characterizing the key geometrical indices of the prostate and identifying the presence of cancerous nodules. Therefore, it has potential, when combined with instrumented palpation, for primary diagnosis of prostate cancer, and, potentially, solid tumors in other types of soft tissue. [ABSTRACT FROM AUTHOR]

Segregation of tumor region in brain MR image is a prominent task that instantly provides easier tumor diagnosis, which leads to effective radiotherapy planning. For decades together, several segmentation methods for a brain tumor have been presented and until now, enhanced tumor segmentation procedure tends to be a challenging task because, MR images are mostly inbred with varied tumor dimensions of disproportioned boundaries. To address this issue, we develop an improved brain image segmentation technique called BAT based Interval Type-2 Fuzzy C-Means (BAT-IT2FCM) clustering. The BAT algorithm is utilized to find out the optimal cluster location from which the clustering operation by Interval Type-2 Fuzzy C-Means (IT2FCM) is performed. The optimal cluster location pointed/identified by the BAT algorithm helps in easing the clustering operation performed by IT2FCM algorithm, and thereby reducing computational complexity. The efficient outcome from BAT-IT2FCM methodology was affirmed using the performance metrics such as computational time, Peak Signal to Noise Ratio, Mean Squared Error, Jaccard Tanimoto Co-efficient Index and Dice Overlap Index. Also, segmentation results of clinical brain MR images produced by the proposed methodology were evaluated with the support from radiologists (Gold Standard). The suggested BAT based fuzzy related clustering produces sensitivity and specificity values of 98.56 ± 1.2 and 97.67 ± 1.3, respectively, which are better than the existing techniques used for brain image segmentation. Heterogeneous tumor types of different grade levels and tissue structures present in the brain MR slices of three different axes are precisely segmented by the proposed methodology for better visualization of oncologists. [ABSTRACT FROM AUTHOR]

Highlights • Differentiating between benign and malignant tissues based on image intensities. • Solve the topological problems, the white and the gray areas in patient's image. • Formulate the theoretical data optimization problem by applying curve evolution. • The proposed algorithm saves the operation time in addition to improving accuracy. Abstract Identification of tumor in medical images is an important task which leads to increased survival chances. In many cases, tumor diagnosis in medical images is very complicated since the pixel intensities between benign and malignant tissues may be very close. The identification problem is defined as the intensification of the mutual data between the image pixel intensities and the desired areas (tumors in this paper), depending on a limitation of the overall size of the desired area borders. This paper introduces a new approach for image segmentation in differentiating between benign and malignant tissues through a nonparametric approach. The proposed approach assumes that the probability densities correlated with pixel intensities in the image for each region are not already known. The image intensity is used instead of using the probability. A nonparametric density estimation is proposed and formulates the theoretical data optimization problem by applying curve evolution methods and deriving the correlated gradient flows. It uses level-set techniques to achieve the resulting evolution. The algorithm is applied to 100 sets of different real data in patient images with benign or/and malignant tissues. The experimental results show that the proposed algorithm proved effective in tumor identification with an acceptable accuracy of 93.1%, especially in-patient images diagnosed with carcinoma at an early stage. Graphical abstract Image, graphical abstract In this technique, m level-set functions are used to segment up to 2m areas, and the equation of the resulting evolution curve is proving to be an actual generalization of non-parametric area competition as shown in Fig. 4. [ABSTRACT FROM AUTHOR]

Development of high selectivity, accurate targeted and noninvasive fluorescent probe for monitoring specific enzyme activity associated with the tumor is urgent needed for early diagnosis of cancer and clinical fluorescence interventional resection guidance treatment. Owing to the invasion of malignant tumor cells, tumor cells are mixed with normal cells in the actual tumor location, which make it quite difficult for clinician to diagnose early diagnosis of tumor as well as resection of tumor. To overcome aforementioned obstacle, herein, an ingenious enzyme-activated one and two-photon fluorescent probe TCF-GGT was constructed and synthesized including γ-GGT enzyme specific identification site and far-red fluorophore for imaging. Under simulative physiological condition, probe TCF-GGT demonstrated high selectivity, sensitivity (DL 0.014 mU/mL), rapid response ( T e 14 min) for the detection of γ-GGT enzyme. By virtue of its biocompatibility, probe was employed for the identification of ovarian cancer cells (A2780 cells) from normal cells (NIH-3T3 cells), particularly in mixed cultivation dish (simulate the actual environment of tumor) through 2D&3D fluorescence imaging with “dual” mark (Nucleic acid labeling used by Hoechst 33342 dye and γ-GGT enzyme labeling used by probe TCF-GGT) for the first time. Probe TCF-GGT could be visualize endogenous γ-GGT activity in HepG-2 cells and zebrafish on the two-photon confocal platform, which is conduce to estimate the inhibitor of γ-GGT enzyme in vivo . Through NaBu (a potential anticancer drug) stimulation, the changes of γ-GGT activity were observed in living MCF-7 cells by using this probe. More importantly, the deep tissue penetration ability of far-red fluorescence allowed the two-photon fluorescent probe TCF-GGT to real-time track γ-GGT activity in tissue slices and tumor xenotransplantation model of mice by the tail vein injection, which showed that this enzyme-triggered fluorogenic probe would be a potential tool for preclinical applications of tumor resection. [ABSTRACT FROM AUTHOR]

This article aims at developing an automated hybrid algorithm using Cuckoo Based Search (CBS) and interval type-2 fuzzy based clustering, so as to exhibit efficient magnetic resonance (MR) brain image segmentation. An automatic MR brain image segmentation facilitates and enables a radiologist to have a brief review and easy analysis of complicated tumor regions of imprecise gray level regions with minimal user interface. The tumor region having severe intensity variations and suffering from poor boundaries are to be detected by the proposed hybrid technique that could ease the process of clinical diagnosis and this tends to be the core subject of this article. The ability of the proposed technique is compared using standard comparison parameters such as mean squared error, peak signal to noise ratio, computational time, Dice Overlap Index, and Jaccard Tanimoto Coefficient Index. The proposed CBS combined with interval type-2 fuzzy based clustering produces a sensitivity of 0.7143 and specificity of 0.9375, which are far better than the conventional techniques such as kernel based, entropy based, graph-cut based, and self-organizing maps based clustering. Appreciable segmentation results of tumor region that enhances clinical diagnosis is made available through this article and two of the radiologists who have hands on experience in the field of radiology have extended their support in validating the efficiency of the proposed methodology and have given their consent in utilizing the proposed methodology in the processes of clinical oncology. [ABSTRACT FROM AUTHOR]

ABSTRACT Tumor and Edema region present in Magnetic Resonance (MR) brain image can be segmented using Optimization and Clustering merged with seed-based region growing algorithm. The proposed algorithm shows effectiveness in tumor detection in T1 - w, T2 - w, Fluid Attenuated Inversion Recovery and Multiplanar Reconstruction type MR brain images. After an initial level segmentation exhibited by Modified Particle Swarm Optimization (MPSO) and Fuzzy C - Means (FCM) algorithm, the seed points are initialized using the region growing algorithm and based on these seed points; tumor detection in MR brain images is done. The parameters taken for comparison with the conventional techniques are Mean Square Error, Peak Signal to Noise Ratio, Jaccard (Tanimoto) index, Dice Overlap indices and Computational Time. These parameters prove the efficacy of the proposed algorithm. Heterogeneous type tumor regions present in the input MR brain images are segmented using the proposed algorithm. Furthermore, the algorithm shows augmentation in the process of brain tumor identification. Availability of gold standard images has led to the comparison of the suggested algorithm with MPSO-based FCM and conventional Region Growing algorithm. Also, the algorithm recommended through this research is capable of producing Similarity Index value of 0.96, Overlap Fraction value of 0.97 and Extra Fraction value of 0.05, which are far better than the values articulated by MPSO-based FCM and Region Growing algorithm. The proposed algorithm favors the segmentation of contrast enhanced images. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 33-45, 2017 [ABSTRACT FROM AUTHOR]

A new approach to tumor discrimination using magnetic resonance imaging is reported. The susceptibility difference between venous blood and the surrounding tissue is used to generate contrast. This approach is able to exquisitely reveal small veins even those which are smaller than a voxel. Using this method, it is possible to visualize the primary draining veins within tumors better than contrast enhanced magnetic resonance imaging methods that require a contrast agent or even conventional angiography. The ability to highlight deoxygenated blood may lead to the possibility of differentiating benign from malignant tumors noninvasively.

Gene selection is crucial to tumor identification based on microarray expression data. The identification of genes with strong discriminative power has been a hot research topic and a great challenge in research related to machine learning-based tumor identification. This study proposes a novel hybrid gene selection strategy for tumor identification by combining multifilter integration and a recursive flower pollination algorithm (MFI-RFPA) to select a minimum number of genes to achieve optimal classification performance. MFI-RFPA selects genes that involve two stages. In the first stage, most irrelevant and noise genes are eliminated by using a combination of three filters based on different metrics. In this process, multiple filters based on different metrics are combined in the voting, which makes the filter process of MFI-RFPA more robust. In the second stage, MFI-RFPA searches for the optimal subset of the remaining genes by using a recursive flower pollination algorithm (RFPA). In this process, the flower pollination algorithm is performed recursively without reducing the classification accuracy, which ensures that a small and compact subset of genes with excellent classification performance is searched. In the experimental part, MFI-RFPA is compared with both its directly related methods (such as MFI, FPA, RFPA, MFI-FPA) and other state-of-the-art methods. The excellent performance of MFI-RFPA in reducing the number of genes and improving the classification accuracy can be reflected in the comparison of experiments. [ABSTRACT FROM AUTHOR]

Background: Indocyanine green (ICG) fluorescence-guided surgery is a real-time navigation technology for tumor detection, securing surgical margins, segmentation mapping, and cholangiography in liver surgery [1]. According to recent reports, the Medical Imaging Projection System (MIPS) may be a useful new real-time navigation technology for open anatomical liver resection [2]. However, the efficacy of MIPS for tumor identification, cholangiography, and securing surgical margins is uncertain. In this report, we introduce MIPS-assisted liver resection for real-time navigation during simultaneous tumor identification, cholangiography, and securing surgical margins., Methods: A 76-year-old man presented with a 30 × 30 mm recurrent hepatocellular carcinoma on the transection plane after right anterior sectionectomy. Eight radiofrequency ablations were performed after the first hepatectomy. Preoperative computed tomography and three-dimensional simulation revealed a tumor near the posterior Glissonean branch. One day before surgery, 2.5 mg/body ICG was administered. We analyzed whether MIPS could simultaneously facilitate tumor identification, cholangiography, and securing surgical margins. The relationship between fluorescent imaging and the surgical margin was evaluated with a fluorescent microscope [3]., Results: Simultaneous tumor identification, cholangiography, and securing the surgical margins were demonstrated by adjusting the image projection of MIPS, and R0 resection was achieved without biliary injury (Figs. 1 and 2). The operative time and estimated blood loss were 287 minutes and 394 mL, respectively. He was discharged on postoperative day 12 without any complications., Conclusion: MIPS could be useful for real-time navigation for tumor identification, cholangiography, and securing surgical margins during liver surgery. The threshold of fluorescent intensity should be set for optimal image projection., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

This paper explores methods that make use of visual cues aimed at generating actual haptic sensation to the user, namely pseudo-haptics. We propose a new pseudo-haptic feedback-based method capable of conveying 3D haptic information and combining visual haptics with force feedback to enhance the user's haptic experience. We focused on an application related to tumor identification during palpation and evaluated the proposed method in an experimental study where users interacted with a haptic device and graphical interface while exploring a virtual model of soft tissue, which represented stiffness distribution of a silicone phantom tissue with embedded hard inclusions. The performance of hard inclusion detection using force feedback only, pseudo-haptic feedback only, and the combination of the two feedbacks was compared with the direct hand touch. The combination method and direct hand touch had no significant difference in the detection results. Compared with the force feedback alone, our method increased the sensitivity by 5%, the positive predictive value by 4%, and decreased detection time by 48.7%. The proposed methodology has great potential for robot-assisted minimally invasive surgery and in all applications where remote haptic feedback is needed. [ABSTRACT FROM AUTHOR]