Direct electrical stimulation mapping of cognitive functions in the human brain

ABSTRACT

Direct electrical stimulation (DES) is a well-established clinical tool for mapping cognitive functions while patients are undergoing awake neurosurgery or invasive long-term monitoring to identify epileptogenic tissue. Despite the proliferation of a range of invasive and noninvasive methods for mapping sensory, motor and cognitive processes in the human brain, DES remains the clinical gold standard for establishing the margins of brain tissue that can be safely removed while avoiding long-term neurological deficits. In parallel, and principally over the last two decades, DES has emerged as a powerful scientific tool for testing hypotheses of brain organization and mechanistic hypotheses of cognitive function. DES can cause transient “lesions” and thus can support causal inferences about the necessity of stimulated brain regions for specific functions, as well as the separability of sensory, motor and cognitive processes. This Special Issue of Cognitive Neuropsychology emphasizes the use of DES as a research tool to advance understanding of normal brain organization and function.     

Exome sequencing in neonates: diagnostic rates,characteristics, and time to diagnosis

PurposeNeonatal patients are particularly appropriate for utilization of diagnostic exome sequencing (DES), as many Mendelian diseases are known to present in this period of life but often with complex, heterogeneous features. We attempted to determine the diagnostic rates and features of neonatal patients undergoing DES.MethodsThe clinical histories and results of 66 neonatal patients undergoing DES were retrospectively reviewed.ResultsClinical DES identified potentially relevant findings in 25 patients (37.9%). The majority of patients had structural anomalies such as birth defects, dysmorphic features, cardiac, craniofacial, and skeletal defects. The average time for clinical rapid testing was 8 days.ConclusionOur observations demonstrate the utility of family-based exome sequencing in neonatal patients, including familial cosegregation analysis and comprehensive medical review.     

Intervention time prediction from surgical low-level tasks

ObjectiveEffective time and resource management in the operating room requires process information concerning the surgical procedure being performed. A major parameter relevant to the intraoperative process is the remaining intervention time. The work presented here describes an approach for the prediction of the remaining intervention time based on surgical low-level tasks.Materials and methodsA surgical process model optimized for time prediction was designed together with a prediction algorithm. The prediction accuracy was evaluated for two different neurosurgical interventions: discectomy and brain tumor resections. A repeated random sub-sampling validation study was conducted based on 20 recorded discectomies and 40 brain tumor resections.ResultsThe mean absolute error of the remaining intervention time predictions was 13 min 24 s for discectomies and 29 min 20 s for brain tumor removals. The error decreases as the intervention progresses.DiscussionThe approach discussed allows for the on-line prediction of the remaining intervention time based on intraoperative information. The method is able to handle demanding and variable surgical procedures, such as brain tumor resections. A randomized study showed that prediction accuracies are reasonable for various clinical applications.ConclusionThe predictions can be used by the OR staff, the technical infrastructure of the OR, and centralized management. The predictions also support intervention scheduling and resource management when resources are shared among different operating rooms, thereby reducing resource conflicts. The predictions could also contribute to the improvement of surgical workflow and patient care.     

Impact of errors in paper-based and computerized diabetes management with decision support for hospitalized patients with type 2 diabetes. A post-hoc analysis of a before and after study

ObjectiveMost preventable adverse drug events and medication errors occur during medication ordering. Medication order entry and clinical decision support are available on paper or as computerized systems. In this post-hoc analysis we investigated frequency and clinical impact of blood glucose (BG) documentation- and user-related calculation errors as well as workflow deviations in diabetes management. We aimed to compare a paper-based protocol to a computerized medication management system combined with clinical workflow and decision support.MethodsSeventy-nine hospitalized patients with type 2 diabetes mellitus were treated with an algorithm driven basal-bolus insulin regimen. BG measurements, which were the basis for insulin dose calculations, were manually entered either into the paper-based workflow protocol (PaperG: 37 patients) or into GlucoTab^®—a mobile tablet PC based system (CompG: 42 patients). We used BG values from the laboratory information system as a reference. A workflow simulator was used to determine user calculation errors as well as workflow deviations and to estimate the effect of errors on insulin doses. The clinical impact of insulin dosing errors and workflow deviations on hypo- and hyperglycemia was investigated.ResultsThe BG documentation error rate was similar for PaperG (4.9%) and CompG group (4.0%). In PaperG group, 11.1% of manual insulin dose calculations were erroneous and the odds ratio (OR) of a hypoglycemic event following an insulin dosing error was 3.1 (95% CI: 1.4–6.8). The number of BG values influenced by insulin dosing errors was eightfold higher than in the CompG group. In the CompG group, workflow deviations occurred in 5.0% of the tasks which led to an increased likelihood of hyperglycemia, OR 2.2 (95% CI: 1.1–4.6).DiscussionManual insulin dose calculations were the major source of error and had a particularly strong influence on hypoglycemia. By using GlucoTab^®, user calculation errors were entirely excluded. The immediate availability and automated handling of BG values from medical devices directly at the point of care has a high potential to reduce errors. Computerized systems facilitate the safe use of more complex insulin dosing algorithms without compromising usability. In CompG group, missed or delayed tasks had a significant effect on hyperglycemia, while in PaperG group insufficient precision of documentation times limited analysis. The use of old BG measurements was clinically less relevant.ConclusionInsulin dosing errors and workflow deviations led to measurable changes in clinical outcome. Diabetes management systems including decision support should address nurses as well as physicians in a computerized way. Our analysis shows that such systems reduce the frequency of errors and therefore decrease the probability of hypo- and hyperglycemia.     

Redefining the sonography workflow through the application of a departmental computerized workflow management system

PurposeThe purpose of this study is to demonstrate and evaluate the effective application of a computerized workflow management system (WMS) into sonography workflow in order to reduce patient exam waiting time, number of waiting patients, sonographer stress level, and to improve patient satisfaction.MethodsA computerized WMS was built with seamless integration of an automated patient sorting algorithm, a real-time monitoring system, exam schedules fine-tuning, a tele-imaging support system, and a digital signage broadcasting system of patient education programs. The computerized WMS was designed to facilitate problem-solving through continuous customization and flexible adjustment capability. Its effects on operations, staff stress, and patient satisfaction were studied.ResultsAfter implementation of the computerized WMS, there is a significant decrease in patient exam waiting time and sonographer stress level, significant increase in patient satisfaction regarding exam waiting time and the number of examined patients, and marked decrease in the number of waiting patients at different time points in a day.ConclusionThrough multidisciplinary teamwork, the computerized WMS provides a simple and effective approach that can overcome jammed exams associated problems, increase patient satisfaction level, and decrease staff workload stress under limited resources, eventually creating a win–win situation for both the patients and radiology personnel.     

乙烯雌酚诱导的大鼠垂体瘤模型的建立

目的:观察己烯雌酚诱导大鼠垂体瘤成瘤的稳定性,为后继研究提供实验基础。方法:60只标准Wistar-Furth大鼠随机分成己烯雌酚注射组、灭菌花生油注射组和对照组,每组20只,观察处理后大鼠一般情况、体重及行为学改变,并分别于4周、8周、和12周处死大鼠,观察成瘤情况和垂体重量变化,HE染色及免疫组化染色的方法鉴别肿瘤类型及CD31表达。结果:处理后2周,己烯雌酚注射组大鼠体重增长明显较其余2组缓慢,并出现脱毛、行动缓慢等表现。在相应时点处死大鼠,实验组出现肿瘤形成,4周、8周和12周成瘤率分别为30%、90%和100%,其余2组成瘤率为0。HE染色可见异常细胞。免疫组化鉴别均为催乳素(PRL)腺瘤,部分混杂促肾上腺皮质激素(ACTH)。实验组垂体组织无论成瘤与否,CD31阳性表达率与对照组相比差异显著。成瘤与否跟大鼠性别无关。结论:腹腔注射己烯雌酚诱导Wistar-Furth大鼠垂体瘤模型成瘤效果确切稳定。在成瘤过程中,部分指标出现动态变化,可以作为进一步研究的模型。     

Predicting treatment process steps from events

MotivationThe primary economy-driven documentation of patient-specific information in clinical information systems leads to drawbacks in the use of these systems in daily clinical routine. Missing meta-data regarding underlying clinical workflows within the stored information is crucial for intelligent support systems. Unfortunately, there is still a lack of primary clinical needs-driven electronic patient documentation. Hence, physicians and surgeons must search hundreds of documents to find necessary patient data rather than accessing relevant information directly from the current process step. In this work, a completely new approach has been developed to enrich the existing information in clinical information systems with additional meta-data, such as the actual treatment phase from which the information entity originates.MethodsStochastic models based on Hidden Markov Models (HMMs) are used to create a mathematical representation of the underlying clinical workflow. These models are created from real-world anonymized patient data and are tailored to therapy processes for patients with head and neck cancer. Additionally, two methodologies to extend the models to improve the workflow recognition rates are presented in this work.ResultsA leave-one-out cross validation study was performed and achieved promising recognition rates of up to 90% with a standard deviation of 6.4%.ConclusionsThe method presented in this paper demonstrates the feasibility of predicting clinical workflow steps from patient-specific information as the basis for clinical workflow support, as well as for the analysis and improvement of clinical pathways.     

Haploseek: a 24-hour all-in-one method for preimplantation genetic diagnosis (PGD) of monogenic disease and aneuploidy

PurposeTo develop an economical, user-friendly, and accurate all-in-one next-generation sequencing (NGS)-based workflow for single-cell gene variant detection combined with comprehensive chromosome screening in a 24-hour workflow protocol.MethodsWe subjected single lymphoblast cells or blastomere/blastocyst biopsies from four different families to low coverage (0.3×–1.4×) genome sequencing. We combined copy-number variant (CNV) detection and whole-genome haplotype phase prediction via Haploseek, a novel, user-friendly analysis pipeline. We validated haplotype predictions for each sample by comparing with clinical preimplantation genetic diagnosis (PGD) case results or by single-nucleotide polymorphism (SNP) microarray analysis of bulk DNA from each respective lymphoblast culture donor. CNV predictions were validated by established commercial kits for single-cell CNV prediction.ResultsHaplotype phasing of the single lymphoblast/embryo biopsy sequencing data was highly concordant with relevant ground truth haplotypes in all samples/biopsies from all four families. In addition, whole-genome copy-number assessments were concordant with the results of a commercial kit.ConclusionOur results demonstrate the establishment of a reliable method for all-in-one molecular and chromosomal diagnosis of single cells. Important features of the Haploseek pipeline include rapid sample processing, rapid sequencing, streamlined analysis, and user-friendly reporting, so as to expedite clinical PGD implementation.     

Candidate-gene criteria for clinical reporting: diagnostic exome sequencing identifies altered candidate genes among 8% of patients with undiagnosed diseases

PurposeDiagnostic exome sequencing (DES) is now a commonly ordered test for individuals with undiagnosed genetic disorders. In addition to providing a diagnosis for characterized diseases, exome sequencing has the capacity to uncover novel candidate genes for disease.MethodsFamily-based DES included analysis of both characterized and novel genetic etiologies. To evaluate candidate genes for disease in the clinical setting, we developed a systematic, rule-based classification schema.ResultsTesting identified a candidate gene among 7.7% (72/934) of patients referred for DES; 37 (4.0%) and 35 (3.7%) of the genes received evidence scores of “candidate” and “suspected candidate,” respectively. A total of 71 independent candidate genes were reported among the 72 patients, and 38% (27/71) were subsequently corroborated in the peer-reviewed literature. This rate of corroboration increased to 51.9% (27/52) among patients whose gene was reported at least 12 months previously.ConclusionsHerein, we provide transparent, comprehensive, and standardized scoring criteria for the clinical reporting of candidate genes. These results demonstrate that DES is an integral tool for genetic diagnosis, especially for elucidating the molecular basis for both characterized and novel candidate genetic etiologies. Gene discoveries also advance the understanding of normal human biology and more common diseases.     

Physicians’ perspectives on receiving unsolicited genomic results

PurposePhysicians increasingly receive genomic test results they did not order, which we term “unsolicited genomic results” (UGRs). We asked physicians how they think such results will affect them and their patients.MethodsSemistructured interviews were conducted with adult and pediatric primary care and subspecialty physicians at four sites affiliated with a large-scale return-of-results project led by the Electronic Medical Records and Genomics (eMERGE) Network. Twenty-five physicians addressed UGRs and (1) perceived need for actionability, (2) impact on patients, (3) health care workflow, (4) return of results process, and (5) responsibility for results.ResultsPhysicians prioritize actionability of UGRs and the need for clear, evidence-based “paths” for action coupled with clinical decision support (CDS). They identified potential harms to patients including anxiety, false reassurance, and clinical disutility. Clinicians worried about anticipated workflow issues including responding to UGRs and unreimbursed time. They disagreed about who was responsible for responding to UGRs.ConclusionThe prospect of receiving UGRs for otherwise healthy patients raises important concerns for physicians. Their responses informed development of an in-depth survey for physicians following return of UGRs. Strategic workflow integration of UGRs will likely be necessary to empower physicians to serve their patients effectively.     

High density DNA methylation array is a reliable alternative for PCR-based analysis of the MGMT promoter methylation status in glioblastoma

AimMGMT promoter methylation status is an important biomarker predicting survival and response to chemotherapy in patients suffering from glioblastoma. Since new diagnostic methods such as methylome-based classification of brain tumors are more and more frequently performed, we aimed at comparing the suitability of calculating the MGMT promoter methylation status in a quantitative manner from the methylome profiling as compared to the classic gold standard assessment by PCR.MethodsOur cohort consisted of 39 cases diagnosed as “glioblastoma, IDH-wildtype” of which the MGMT promoter methylation status was analyzed with both methylation-specific PCR and high density DNA methylation array using the STP-27 algorithm. Contradictory results were validated by pyrosequencing.ResultsThe inter-method reliability reached 77% (kappa-coefficient: 0.58) when also cases with an inconclusive result in one or the other method were taken into account. When only cases with conclusive results in both methods were considered, a very high inter-method reliability of 91% (kappa-coefficient: 0.86) could be achieved. For “methylated” cases, no contradictory results were obtained. For the remaining two cases with discrepant results subsequent pyrosequencing analyses spoke in favor of each previously applied method once.ConclusionIn addition to its benefits for molecular subgrouping and copy number analysis of brain tumors, DNA-methylation based classification is a highly reliable tool for the assessment of MGMT promoter methylation status in glioblastoma patients.     

Medical students’ communication skills in clinical education: Results from a cohort study

ObjectiveTo assess students’ communication skills during clinical medical education and at graduation.MethodsWe conducted an observational cohort study from 2007 to 2011 with 26 voluntary undergraduate medical students at Hamburg University based on video-taped consultations in year four and at graduation. 176 consultations were analyzed quantitatively with validated and non-validated context-independent communication observation instruments (interrater reliability ≥0.8). Based on observational protocols each consultation was also documented in free-text comments, salient topics were extracted afterwards.Results26 students, seven males, were enrolled in the survey. On average, graduates scored higher in differential-diagnostic questioning and time management but showed deficiencies in taking systematic and complete symptom-oriented histories, in communication techniques, in structuring consultations and in gathering the patients’ perspectives. Patient-centeredness and empathy were rather low at graduation. Individual deficiencies could barely be eliminated.ConclusionMedical students were able to enhance their clinical reasoning skills and their time management. Still, various communication deficiencies in final year students became evident regarding appropriate history taking, communication skills, empathy and patient-centeredness.Practice implicationsThe necessity of developing a longitudinal communication curriculum with enhanced communication trainings and assessments became evident. A curriculum should ensure that students’ communication competencies are firmly achieved at graduation.     

Allergic conjunctivitis and dry eye syndrome

BackgroundAllergic conjunctivitis (AC) and dry eye syndrome (DES) are 2 of the most common anterior inflammatory disorders of the ocular surface and one does not preclude the coexistence of the other.ObjectivesTo examine the potential overlap between AC and DES as comorbidities.MethodsUsing the validated questionnaire known as Subjective Evaluation of Symptom of Dryness, we studied self-reported itchiness, dryness, and redness. In an outpatient optometric setting, 689 patients treated from January 1, 2007, to January 1, 2011, were surveyed for their ocular history and categorized according to their reported level of discomfort of itchiness, dryness, and redness.ResultsPatients ranged in age from 5 to 90 years (median age, 25 years; 39.5% male; 60.5% female). In the studied 689 patients, clinically significant itchiness was found in 194 (28.2%), dry eyes in 247 (35.8%), and redness in 194 (28.2%). Symptom overlap was demonstrated in many of the patients. Of the 194 patients with itchiness, 112 (57.7%) had clinically significant dryness. In the 247 patients with dry eyes, 112 (45.3%) had clinically significant itch. Redness was apparent in 120 of the 194 patients with itch (61.9%) and 122 of the 247 patients with dryness (49.4%). Statistical analysis demonstrated that self-reported itchiness, dryness, and redness were not independent of each other (P<.001; Pearson χ² test). The odds of patients with “itchy eyes” also experiencing dry eyes were 2.11 times and the odds of these patients also experiencing redness were 7.34 times that of patients with nonitchy eyes.ConclusionsMost patients with “itchy eyes” consistent with AC also have dry eyes and redness. These results suggest that some symptomatic patients concomitantly have features of AC and DES.     

A suite of automated sequence analyses reduces the number of candidate deleterious variants and reveals a difference between probands and unaffected siblings

PurposeDevelop an automated exome analysis workflow that can produce a very small number of candidate variants yet still detect different numbers of deleterious variants between probands and unaffected siblings.MethodsNinety-seven outbred nuclear families from the Undiagnosed Diseases Program/Network included single probands and the corresponding unaffected sibling(s). Single-nucleotide polymorphism (SNP) chip and exome analyses were performed on all, with proband and unaffected sibling considered independently as the target. The total burden of candidate genetic variants was summed for probands and siblings over all considered disease models.ResultsExome analysis workflow include automated programs for ethnicity-matched genotype calling, salvage pathway for Mendelian inconsistency, compound heterozygous recessive detection, BAM file regional curation, population frequency filtering, pedigree-aware BAM file noise evaluation, and exon deletion filtration. This workflow relied heavily on BAM file analysis. A greater average pathogenic variant number was found compared with unaffected siblings. This was significant (p < 0.05) when using published recommended thresholds, and implies that causal variants are retained in many probands’ lists.ConclusionUsing Mendelian and non-Mendelian models, this agnostic exome analysis shows a difference between a small group of probands and their unaffected siblings. This workflow produces candidate lists small enough to pursue with laboratory validation.     

Detecting Functional Connectivity in fMRI Using PCA and Regression Analysis

A fMRI connectivity analysis approach combining principal component analysis (PCA) and regression analysis is proposed to detect functional connectivity between the brain regions. By first using PCA to identify clusters within the vectors of fMRI time series, more energy and information features in the signal can be maintained than using averaged values from brain regions of interest. Then, regression analysis can be applied to the extracted principal components in order to further investigate functional connectivity. Finally, t-test is applied and the patterns with t-values lager than a threshold are considered as functional connectivity mappings. The validity and reliability of the presented method were demonstrated with both simulated data and human fMRI data obtained during behavioral task and resting state. Compared to the conventional functional connectivity methods such as average signal based correlation analysis, independent component analysis (ICA) and PCA, the proposed method achieves competitive performance with greater accuracy and true positive rate (TPR). Furthermore, the ‘default mode’ and motor network results of resting-state fMRI data indicate that using PCA may improve upon application of existing regression analysis methods in study of human brain functional connectivity.     

Direct identification of clinical pathogens from liquid culture media by MALDI-TOF MS analysis

ObjectivesWe propose using MALDI-TOF MS as a tool for identifying microorganisms directly from liquid cultures after enrichment of the clinical sample in the media, to obtain a rapid microbiological diagnosis and an adequate administration of the antibiotic therapy in a clinical setting.MethodsTo evaluate this approach, a series of quality control isolates were grown in thioglycollate (TG) broth and brain heart infusion (BHI) broth and extracted under four different protocols before finally being identified by MALDI-TOF MS. After establishing the best extraction protocol, we validated the method in a total of 300 liquid cultures (150 in TG broth and 150 in BHI broth) of different types of clinical samples obtained from two tertiary Spanish hospitals.ResultsThe initial evaluation showed that the extraction protocol including a 5 minute sonication step yielded 100% valid identifications, with an average score value of 2.305. In the clinical validation of the procedure, 98% of the microorganisms identified from the TG broth were correctly identified relative to 97% of those identified from the BHI broth. In 24% of the samples analysed, growth by direct sowing was only successful in the liquid medium, and no growth was observed in the direct solid agar cultures.ConclusionsUse of MALDI-TOF MS plus the sonication-based extraction method enabled direct and accurate identification of microorganisms in liquid culture media in 15 minutes, in contrast to the 24 hours of subculture required for conventional identification, allowing the administration of a targeted antimicrobial therapy.     

Quantification of Adverse Events Associated with Functional MRI Scanning and with Real-Time fMRI-Based Training

Background

Although functional magnetic resonance imaging (fMRI) is in widespread research use, the safety of this approach has not been extensively quantitatively evaluated. Real-time fMRI (rtfMRI)-based training paradigms use fMRI neurofeedback and cognitive strategies to alter regional brain activation, and are currently being evaluated as a novel approach to treat neurological and psychiatric conditions.

Purpose

The purpose of this study is to determine the incidence and severity of any adverse events that might be caused by changes in brain activation brought about through fMRI or through rtfMRI-based training paradigms.

Method

Quantitative adverse event self-report data were obtained from 641 functional imaging scans in 114 chronic pain patients participating in a research clinical trial examining repeated fMRI scans and rtfMRI-based training. Participants recorded potential adverse events during non-scanning baseline, fMRI scanning, or rtfMRI-based training sessions.

Results

There were no significant increases in the number of reported adverse events following fMRI or rtfMRI scanning sessions compared to baseline non-scanning sessions in a chronic pain trial (N?=?88). There were no reported adverse events of any kind for over 90% of sessions during the course of rtfMRI-based training. When adverse events were reported, they were almost exclusively mild or moderate in severity and similar to those observed in a non-scanning baseline session. There was no increase in adverse events reported by participants receiving feedback from any of four brain regions during repeated rtfMRI-based training scans compared to non-scanning baseline sessions. For chronic pain patients completing the rtfMRI-based training paradigm including up to a total of nine scan sessions (N?=?69), neither the number nor severity of reported events increased during the fMRI or rtfMRI scanning portions of the paradigm. There were no significant increases in the number of reported adverse events in participants who withdrew from the study.

Conclusion

Repeated fMRI scanning and rtfMRI training, consisting of repeated fMRI scanning in conjunction with cognitive strategies and real-time feedback from several regions of interest in multiple brain systems to control brain region activation, were not associated with an increase in adverse event number or severity. These results demonstrate the safety of repetitive fMRI scanning paradigms similar to those in use in many laboratories worldwide, as well as the safety rtfMRI-based training paradigms.     

Workflow mining and outlier detection from clinical activity logs

PurposeThe purpose of this study is twofold: (1) to derive a workflow consensus from multiple clinical activity logs and (2) to detect workflow outliers automatically and without prior knowledge from experts.MethodsWorkflow mining is used in this paper to derive consensus workflow from multiple surgical activity logs using tree-guided multiple sequence alignment. To detect outliers, a global pair-wise sequence alignment (Needleman–Wunsch) algorithm is used. The proposed method is validated for Laparoscopic Cholecystectomy (LAPCHOL).ResultsAn activity log is directly derived for each LAPCHOL surgery from laparoscopic video using an already developed instrument tracking tool. We showed that a generic consensus can be derived from surgical activity logs using multi-alignment. In total 26 surgery logs are used to derive the consensus for laparoscopic cholecystectomy. The derived consensus conforms to the main steps of laparoscopic cholecystectomy as described in best practices. Using global pair-wise alignment, we showed that outliers can be detected from surgeries using the consensus and the surgical activity log.ConclusionAlignment techniques can be used to derive consensus and to detect outliers from clinical activity logs. Detecting outliers particularly in surgery is a main step to automatically mine and analyse the underlying cause of these outliers and improve surgical practices.     

Fully automated tumor segmentation based on improved fuzzy connectedness algorithm in brain MR images

Uncontrollable and unlimited cell growth leads to tumor genesis in the brain. If brain tumors are not diagnosed early and cured properly, they could cause permanent brain damage or even death to patients. As in all methods of treatments, any information about tumor position and size is important for successful treatment; hence, finding an accurate and a fully automated method to give information to physicians is necessary.A fully automatic and accurate method for tumor region detection and segmentation in brain magnetic resonance (MR) images is suggested. The presented approach is an improved fuzzy connectedness (FC) algorithm based on a scale in which the seed point is selected automatically. This algorithm is independent of the tumor type in terms of its pixels intensity. Tumor segmentation evaluation results based on similarity criteria (similarity index (SI), overlap fraction (OF), and extra fraction (EF) are 92.89%, 91.75%, and 3.95%, respectively) indicate a higher performance of the proposed approach compared to the conventional methods, especially in MR images, in tumor regions with low contrast. Thus, the suggested method is useful for increasing the ability of automatic estimation of tumor size and position in brain tissues, which provides more accurate investigation of the required surgery, chemotherapy, and radiotherapy procedures.