A novel predictive marker for placental abruption with composite adverse outcomes: creatinine-fibrinogen ratio.

PURPOSE: To develop a new cost-effective marker named creatinine-fibrinogen ratio (CFR) for the prediction of composite adverse outcomes (CAO) in placental abruption cases. METHODS: A total of 109 placental abruption patients (30 with adverse outcomes, 79 without adverse outcomes) were enrolled in this retrospective cohort study. Patients with at least one of the features listed below were included in the abruption with CAO group: requirement of blood product transfusion (erythrocyte suspension, fresh frozen plasma, pooled thrombocyte, thrombocyte apheresis), development of acute kidney injury or disseminated intravascular coagulation, and need for intensive care unit. Laboratory parameters and CFR values at admission to the hospital were compared between the two groups. RESULTS: Higher creatinine and lower fibrinogen levels were found in the CAO group (p = 0.007 and p < 0.001 respectively). The CFR value of the CAO group was significantly higher (p < 0.001). In the ROC curve analysis performed to investigate the value of CFR in CAO prediction, the area under the curve (AUC) was calculated as 0,802 (95% CI 0.709-0.895, 77% sensitivity, 65% specificity). CONCLUSION: CFR seems to be a practical marker for the prediction of CAOs in pregnant women with ablatio placenta.

The impact of gestational diabetes on the development of fetal frontal lobe: A case-control study from a tertiary center.

PURPOSE: To determine the effects of gestational diabetes mellitus (GDM) on fetal frontal lobe development. METHODS: This study was conducted prospectively between May 2023 and August 2023 in Ankara City Hospital perinatology clinic. Maternal age, maternal body mass index (BMI), gestational week (GW), biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femur length (FL), estimated fetal weight (EFW), frontal antero-posterior diameter (FAPD), occipito-frontal diameter (OFD), FAPD/OFD ratio, and FAPD/HC ratio, were compared between GDM (n = 40) and low risk controls (n = 56). RESULTS: The mean maternal age was found higher in the GDM group compared to control group (p = 0.002). Maternal BMI was significantly higher in the GDM group than the control group (p = 0.01). Abdominal circumference (AC) was significantly higher in the GDM group compared to control group (p = 0.04). EFW was significantly higher in the GDM group compared to control group (p = 0.04). FAPD/OFD ratio was found to be higher in the GDM group than in the control group (p = 0.001). Among GDM patients, no statistically significant difference was found in the ultrasound measurements between the groups receiving insulin treatment and those without treatment. According to the correlation analysis results a moderate, positive, and statistically significant correlation was present between FAPD/OFD and GDM. In perinatal outcomes, the rate of neonatal intensive care unit admission was significantly higher in the GDM group. DISCUSSION: Fetal frontal lobe development seems to be affected by GDM.

The Association of Serum Midkine Level with Invasion in Placenta Previa: A Case-Control Study from a Tertiary Reference Center.

We aimed to examine the relationship between serum midkine levels and placental invasion in pregnant women with placenta previa. The study group consisted of 43 pregnant women diagnosed with placenta previa, whereas the control group consisted of 60 healthy pregnant women. Serum midkine levels were compared between pregnant women with placenta previa and the control group in this study's first part. Thereafter, the utility of midkine in the prediction of the abnormally invasive placenta (AIP) was investigated and optimal cutoff values were calculated. Significantly higher serum midkine level was observed in placenta previa cases than in the controls (1.16 ng/mL vs. 0.18 ng/mL, P < 0.001). Serum midkine level was also significantly higher in the AIP group among the placenta previa cases (P = 0.004). In the receiver operating characteristic analysis, the cutoff value of the midkine level in predicting AIP was 1.19 ng/mL. This study revealed that the serum midkine level is higher in pregnant women with AIP. Maternal serum midkine level may be used as a complementary biomarker to the radiological and clinical findings for the prediction of the AIP in placenta previa cases.

Comparison of H-Hayman uterine compression suture with conventional vertical sutures: A cross-sectional study in a tertiary center.

OBJECTIVE: To compare H-Hayman, a modified uterine compression suturing technique (UCS) that we describe for the first time in the literature, with conventional vertical UCS techniques. METHODS: The H-Hayman technique was used in 14 women and the conventional UCS technique in 21 women. In order to provide standardization in the study, only patients who had developed upper-segment atony during cesarean section were recruited for the study. RESULTS: Bleeding control was achieved in 85.7% (12/14) of the cases using the H-Hayman technique. In the remaining two patients with persistent hemorrhage in this group, bleeding control was provided with bilateral uterine artery ligation, and a hysterectomy was avoided in all cases. With the conventional technique, bleeding control was achieved in 76.1% (16/21) of the patients, and the overall success rate was 95.2% after bilateral uterine artery ligation in those with persistent hemorrhage. In addition, the estimated blood loss and the need for erythrocyte suspension transfusion were significantly lower in the H-Hayman group (P = 0.01 and P = 0.04, respectively). CONCLUSION: We found the H-Hayman technique to be at least as successful as conventional UCS. In addition, patients who underwent suturing with the H-Hayman technique had less blood loss and a lower requirement for erythrocyte suspension transfusion.

Radiomics method in the differential diagnosis of diabetic foot osteomyelitis and charcot neuroarthropathy.

OBJECTIVES: Our study used a radiomics method to differentiate bone marrow signal abnormality (BMSA) between Charcot neuroarthropathy (CN) and osteomyelitis (OM). METHODS AND MATERIALS: The records of 166 patients with diabetic foot suspected CN or OM between January 2020 and March 2022 were retrospectively examined. A total of 41 patients with BMSA on MRI were included in this study. The diagnosis of OM was confirmed histologically in 24 of 41 patients. We clinically followed 17 patients as CN with laboratory tests. We also included 29 nondiabetic patients with traumatic (TR) BMSA on MRI as the third group. Contours of all BMSA on T 2 - and T1 -weighted images in three patient groups were segmented semi-automatically on ManSeg (v.2.7d). The T1 and T2 features of three groups in radiomics were statistically evaluated. We applied the multi-class classification (MCC) and binary-class classification (BCC) methodologies to compare results. RESULTS: For MCC, the accuracy of Multi-Layer Perceptron (MLP) was 76.92% and 84.38% for T1 and T2, respectively. According to BCC, for CN, OM, and TR BMSA, the sensitivity of MLP is 74%, 89.23%, and 76.19% for T1, and 90.57%, 85.92%, 86.81% for T2, respectively. For CN, OM, and TR BMSA, the specificity of MLP is 89.16%, 87.57%, and 90.72% for T1 and 93.55%, 89.94%, and 90.48% for T2 images, respectively. CONCLUSION: In diabetic foot, the radiomics method can differentiate the BMSA of CN and OM with high accuracy. ADVANCES IN KNOWLEDGE: The radiomics method can differentiate the BMSA of CN and OM with high accuracy.

Comparison of tomographic kidney volumes measured by semiautomatic segmentation method with scintigraphic split renal function in predicting posttransplant kidney functions.

INTRODUCTION: In this study, it was aimed to compare scintigraphic split renal function (SRF) and computed tomographic (CT) kidney volumes by semiautomatic segmentation method in predicting graft functions after kidney transplantation. METHODS: One hundred and twelve patients (77 males, 35 females) who had a living-donor kidney transplant between 2015 and 2017 in our centre were included in the study. While SRF was calculated with technetium-99m-diethylenetriaminepentaacetic acid (99m Tc-DTPA) scintigraphy, CT angiography was used for volumetric calculations. RESULTS: CT-volumetric measurements, especially renal cortical volume (RCV: 103.8 ± 20 ml) and ratio to body mass index (RCV/BMI: 4.45 ± 1.3) were found to be more significant than 99m Tc-DTPA-SRF in predicting graft functions. The correlations between SRF and RCV with 6th-month estimated glomerular filtration rate (eGFR) (rSRF: 0.052, rRCV: 0.317, p = 0.041) and 1st-year eGFR (rSRF: 0.104, rRCV: 0.374, p = 0.033) were found to be more significant in favour of RCV. The correlation between SRF/BMI and RCV/BMI with 1st-, 6th- and 12th-month eGFR (respectively, p = 0.02/0.048/0.024) were found to be more significant in favour of RCV/BMI. Although univariate analysis showed a significant relationship between most volumetric measurements and 1st-year graft functions, in multivariate analysis only RCV [odds ratio (OR): 1.04 (1.01-1.07), p = 0.023] and RCV/BMI [OR: 2.5 (1.27-5.39), p = 0.013] showed a significant relationship between graft functions. CONCLUSION: In our study, it was shown that CT-based renal volumetric measurements, especially RCV and RCV/BMI, predicted graft functions more strongly than scintigraphic 99m Tc-DTPA-SRF.

Effect of brightness of visual stimuli on EEG signals.

The aim of this study was to examine brightness effect, which is the perceptual property of visual stimuli, on brain responses obtained during visual processing of these stimuli. For this purpose, brain responses of the brain to changes in brightness were explored comparatively using different emotional images (pleasant, unpleasant and neutral) with different luminance levels. In the study, electroencephalography recordings from 12 different electrode sites of 31 healthy participants were used. The power spectra obtained from the analysis of the recordings using short time Fourier transform were analyzed, and a statistical analysis was performed on features extracted from these power spectra. Statistical findings were compared with those obtained from behavioral data. The results showed that the brightness of visual stimuli affected the power of brain responses depending on frequency, time and location. According to the statistically verified findings, the increase in the brightness of pleasant and neutral images increased the average power of responses in the parietal and occipital regions whereas the increase in the brightness of unpleasant images decreased the average power of responses in these regions. Moreover, the statistical results obtained for unpleasant images were found to be in accordance with the behavioral data. The results revealed that the brightness of visual stimuli could be represented by changing the activity power of the brain cortex. The findings emphasized that the brightness of visual stimuli should be viewed as an important parameter in studies using emotional image techniques such as image classification, emotion evaluation and neuro-marketing.

A New Practical Intracerebral Hematoma Volume Calculation Method and Comparison to simple ABC/2.

AIM: To find a more practical and effective formula than simple ABC/2 (sABC/2) to calculate the hematoma volume in patients with subdural and parenchymal haemorrhage. MATERIAL AND METHODS: We reviewed the records of 157 patients who underwent brain computed tomography examinations for stroke from January to October 2017. Our method, sABC/2 formula, and the planimetric method (the gold standard) were used for measuring the volumes of hematoma. RESULTS: The concordance in brain hematoma volumes calculated by sABC/2 and the proposed method as compared to planimetry were 0.92 and 0.93, respectively (p < 0.05). The proposed method calculates the subdural hematoma volumes much better than the conventional one, and the root mean square error (RMSE) values were 32.17 and 20.62 ml for sABC/2 and our new method, respectively, whereas the RMSE values for parenchymal hematomas were 25.01 and 20.46 ml for sABC/2 and our new method, respectively. CONCLUSION: Our new formula for calculating the volume of subdural and parenchymal hematomas is as practical as sABC/2 and allows the clinician to apply the method bedside.

Automated segmentation of the injured kidney due to abdominal trauma.

The objective of this study is to propose and validate a computer-aided segmentation system which performs the automated segmentation of injured kidney in the presence of contusion, peri-, intra-, sub-capsular hematoma, laceration, active extravasation and urine leak due to abdominal trauma. In the present study, total multi-phase CT scans of thirty-seven cases were used; seventeen of them for the development of the method and twenty of them for the validation of the method. The proposed algorithm contains three steps: determination of the kidney mask using Circular Hough Transform, segmentation of the renal parenchyma of the kidney applying the symmetry property to the histogram, and estimation of the kidney volume. The results of the proposed method were compared using various metrics. The kidney quantification led to 92.3 ± 4.2% Dice coefficient, 92.8 ± 7.4%/92.3 ± 5.1% precision/sensitivity, 1.4 ± 0.6 mm/2.0 ± 1.0 mm average surface distance/root-mean-squared error for intact and 87.3 ± 8.4% Dice coefficient, 84.3 ± 13.8%/92.2 ± 3.8% precision/sensitivity and 2.4 ± 2.2 mm/4.0 ± 4.2 mm average surface distance/root-mean-squared error for injured kidneys. The segmentation of the injured kidney was satisfactorily performed in all cases. This method may lead to the automated detection of renal lesions due to abdominal trauma and estimate the intraperitoneal blood amount, which is vital for trauma patients.

A CAD of fully automated colonic polyp detection for contrasted and non-contrasted CT scans.

PURPOSE: Computer-aided detection (CAD) systems are developed to help radiologists detect colonic polyps over CT scans. It is possible to reduce the detection time and increase the detection accuracy rates by using CAD systems. In this paper, we aimed to develop a fully integrated CAD system for automated detection of polyps that yields a high polyp detection rate with a reasonable number of false positives. METHODS: The proposed CAD system is a multistage implementation whose main components are: automatic colon segmentation, candidate detection, feature extraction and classification. The first element of the algorithm includes a discrete segmentation for both air and fluid regions. Colon-air regions were determined based on adaptive thresholding, and the volume/length measure was used to detect air regions. To extract the colon-fluid regions, a rule-based connectivity test was used to detect the regions belong to the colon. Potential polyp candidates were detected based on the 3D Laplacian of Gaussian filter. The geometrical features were used to reduce false-positive detections. A 2D projection image was generated to extract discriminative features as the inputs of an artificial neural network classifier. RESULTS: Our CAD system performs at 100% sensitivity for polyps larger than 9 mm, 95.83% sensitivity for polyps 6-10 mm and 85.71% sensitivity for polyps smaller than 6 mm with 5.3 false positives per dataset. Also, clinically relevant polyps ([Formula: see text]6 mm) were identified with 96.67% sensitivity at 1.12 FP/dataset. CONCLUSIONS: To the best of our knowledge, the novel polyp candidate detection system which determines polyp candidates with LoG filters is one of the main contributions. We also propose a new 2D projection image calculation scheme to determine the distinctive features. We believe that our CAD system is highly effective for assisting radiologist interpreting CT.

Computer-Aided Detection (CADx) for Plastic Deformation Fractures in Pediatric Forearm.

Bowing fractures are incomplete fractures of tubular long bones, often observed in pediatric patients, where plain radiographic film is the non-invasive imaging modality of choice in routine radiological workflow. Due to weak association between bent bone and distinct cortex disruption, bowing fractures may not be diagnosed properly while reading plain radiography. Missed fractures and dislocations are common in accidents and emergency practice, particularly in children. These missed injuries can result in more complicated treatment or even long-term disability. The most common reason for missed fractures is that junior radiologists or physicians lack expertise in pediatric skeletal injury diagnosis. Not only is additional radiation exposure inevitable in the case of misdiagnosis, but other consequences include the patient's prolonged uncomfortableness and possible unnecessary surgical procedures. Therefore, a computerized image analysis system, which would be secondary to the radiologists' interpretations, may reduce adverse effects and improve the diagnostic rates of bowing fracture (detection and quantification). This system would be highly desirable and particularly useful in emergency rooms. To address this need, we investigated and developed a new Computer Aided Detection (CADx) system for pediatric bowing fractures. The proposed system has been tested on 226 cases of pediatric forearms with bowing fractures with respect to normal controls. Receiver operation characteristic (ROC) curves show that the sensitivity and selectivity of the developed CADx system are satisfactory and promising. A clinically feasible graphical user interface (GUI) was developed to serve the practical needs in the emergency room as a diagnostic reference. The developed CADx system also has strong potential to train radiology residents for diagnosing pediatric forearm bowing fractures.

The effect of recording site on extracted features of motor unit action potential.

Motor unit action potential (MUAP), which consists of individual muscle fiber action potentials (MFAPs), represents the electrical activity of the motor unit. The values of the MUAP features are changed by denervation and reinnervation in neurogenic involvement as well as muscle fiber loss with increased diameter variability in myopathic diseases. The present study is designed to investigate how increased muscle fiber diameter variability affects MUAP parameters in simulated motor units. In order to detect this variation, simulated MUAPs were calculated both at the innervation zone where the MFAPs are more synchronized, and near the tendon, where they show increased temporal dispersion. Reinnervation in neurogenic state increases MUAP amplitude for the recordings at both the innervation zone and near the tendon. However, MUAP duration and the number of peaks significantly increased in a case of myopathy for recordings near the tendon. Furthermore, of the new features, "number of peaks×spike duration" was found as the strongest indicator of MFAP dispersion in myopathy. MUAPs were also recorded from healthy participants in order to investigate the biological counterpart of the simulation data. MUAPs which were recorded near to tendon revealed significantly prolonged duration and decreased amplitude. Although the number of peaks was increased by moving the needle near to tendon, this was not significant.

Automated segmentation of the injured spleen.

PURPOSE: To develop a novel automated method for segmentation of the injured spleen using morphological properties following abdominal trauma. Average attenuation of a normal spleen in computed tomography (CT) does not vary significantly between subjects. However, in the case of solid organ injury, the shape and attenuation of the spleen on CT may vary depending on the time and severity of the injury. Timely assessment of the severity and extent of the injury is of vital importance in the setting of trauma. METHODS: We developed an automated computer-aided method for segmenting the injured spleen from CT scans of patients who had splenectomy due to abdominal trauma. We used ten subjects to train our computer-aided diagnosis (CAD) method. To validate the CAD method, we used twenty subjects in our testing group. Probabilistic atlases of the spleens were created using manually segmented data from ten CT scans. The organ location was modeled based on the position of the spleen with respect to the left side of the spine followed by the extraction of shape features. We performed the spleen segmentation in three steps. First, we created a mask of the spleen, and then we used this mask to segment the spleen. The third and final step was the estimation of the spleen edges in the presence of an injury such as laceration or hematoma. RESULTS: The traumatized spleens were segmented with a high degree of agreement with the radiologist-drawn contours. The spleen quantification led to [Formula: see text] volume overlap, [Formula: see text] Dice similarity index, [Formula: see text] precision/sensitivity, [Formula: see text] volume estimation error rate, [Formula: see text] average surface distance/root-mean-squared error. CONCLUSIONS: Our CAD method robustly segments the spleen in the presence of morphological changes such as laceration, contusion, pseudoaneurysm, active bleeding, periorgan and parenchymal hematoma, including subcapsular hematoma due to abdominal trauma. CAD of the splenic injury due to abdominal trauma can assist in rapid diagnosis and assessment and guide clinical management. Our segmentation method is a general framework that can be adapted to segment other injured solid abdominal organs.

A novel method for pulmonary embolism detection in CTA images.

In this paper, we propose a new computer-aided detection (CAD) - based method to detect pulmonary embolism (PE) in computed tomography angiography images (CTAI). Since lung vessel segmentation is the main objective to provide high sensitivity in PE detection, this method performs accurate lung vessel segmentation. To concatenate clogged vessels due to PEs, the starting region of PEs and some reference points (RPs) are determined. These RPs are detected according to the fixed anatomical structures. After lung vessel tree is segmented, the region, intensity, and size of PEs are used to distinguish them. We used the data sets that have heart disease or abnormal tissues because of lung disease except PE in this work. According to the results, 428 of 450 PEs, labeled by the radiologists from 33 patients, have been detected. The sensitivity of the developed system is 95.1% at 14.4 false positive per data set (FP/ds). With this performance, the proposed CAD system is found quite useful to use as a second reader by the radiologists.

Classification of juvenile myoclonic epilepsy data acquired through scanning electromyography with machine learning algorithms.

In this paper, classification of Juvenile Myoclonic Epilepsy (JME) patients and healthy volunteers included into Normal Control (NC) groups was established using Feed-Forward Neural Networks (NN), Support Vector Machines (SVM), Decision Trees (DT), and Naïve Bayes (NB) methods by utilizing the data obtained through the scanning EMG method used in a clinical study. An experimental setup was built for this purpose. 105 motor units were measured. 44 of them belonged to JME group consisting of 9 patients and 61 of them belonged to NC group comprising ten healthy volunteers. k-fold cross validation was applied to train and test the models. ROC curves were drawn for k values of 4, 6, 8 and 10. 100% of detection sensitivity was obtained for DT, NN, and NB classification methods. The lowest FP number, which was obtained by NN, was 5.

Computerized lung nodule detection using 3D feature extraction and learning based algorithms.

In this paper, a Computer Aided Detection (CAD) system based on three-dimensional (3D) feature extraction is introduced to detect lung nodules. First, eight directional search was applied in order to extract regions of interests (ROIs). Then, 3D feature extraction was performed which includes 3D connected component labeling, straightness calculation, thickness calculation, determining the middle slice, vertical and horizontal widths calculation, regularity calculation, and calculation of vertical and horizontal black pixel ratios. To make a decision for each ROI, feed forward neural networks (NN), support vector machines (SVM), naive Bayes (NB) and logistic regression (LR) methods were used. These methods were trained and tested via k-fold cross validation, and results were compared. To test the performance of the proposed system, 11 cases, which were taken from Lung Image Database Consortium (LIDC) dataset, were used. ROC curves were given for all methods and 100% detection sensitivity was reached except naive Bayes.

Colonic polyp detection in CT colonography with fuzzy rule based 3D template matching.

In this paper, we introduced a computer aided detection (CAD) system to facilitate colonic polyp detection in computer tomography (CT) data using cellular neural network, genetic algorithm and three dimensional (3D) template matching with fuzzy rule based tresholding. The CAD system extracts colon region from CT images using cellular neural network (CNN) having A, B and I templates that are optimized by genetic algorithm in order to improve the segmentation performance. Then, the system performs a 3D template matching within four layers with three different cell of 8 x 8, 12 x 12 and 20 x 20 to detect polyps. The CAD system is evaluated with 1043 CT colonography images from 16 patients containing 15 marked polyps. All colon regions are segmented properly. The overall sensitivity of proposed CAD system is 100% with the level of 0.53 false positives (FPs) per slice and 11.75 FPs per patient for the 8 x 8 cell template. For the 12 x 12 cell templates, detection sensitivity is 100% at 0.494 FPs per slice and 8.75 FPs per patient and for the 20 x 20 cell templates, detection sensitivity is 86.66% with the level of 0.452 FPs per slice and 6.25 FPs per patient.

Nodule detection in a lung region that's segmented with using genetic cellular neural networks and 3D template matching with fuzzy rule based thresholding.

OBJECTIVE: The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels. MATERIALS AND METHODS: Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. The 3D ROI image was obtained by combining all the 2-Dimensional (2D) ROI images. A 3D template was created to find the nodule-like structures on the 3D ROI image. Convolution of the 3D ROI image with the proposed template strengthens the shapes that are similar to those of the template and it weakens the other ones. Finally, fuzzy rule based thresholding was applied and the ROI's were found. To test the system's efficiency, we used 16 cases with a total of 425 slices, which were taken from the Lung Image Database Consortium (LIDC) dataset. RESULTS: The computer aided diagnosis (CAD) system achieved 100% sensitivity with 13.375 FPs per case when the nodule thickness was greater than or equal to 5.625 mm. CONCLUSION: Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computer-aided detection of lung nodules.

A preliminary study on computerized lesion localization in MR mammography using 3D nMITR maps, multilayer cellular neural networks, and fuzzy c-partitioning.

Cellular neural networks (CNNs) are massively parallel cellular structures with learning abilities. They can be used to realize complex image processing applications efficiently and in almost real time. In this preliminary study, we propose a novel, robust, and fully automated system based on CNNs to facilitate lesion localization in contrast-enhanced MR mammography, a difficult task requiring the processing of a large number of images with attention paid to minute details. The data set consists of 1170 slices containing one precontrast and five postcontrast bilateral axial MR mammograms from 39 patients with 37 malignant and 39 benign mass lesions acquired using a 1.5 Tesla MR scanner with the following parameters: 3D FLASH sequence, TR/TE 9.80/4.76 ms, flip angle 250, slice thickness 2.5 mm, and 0.625 x 0.625 mm2 in-plane resolution. Six hundred slices with 21 benign and 25 malignant lesions of this set are used for training the CNNs; the remaining data are used for test purposes. The breast region of interest is first segmented from precontrast images using four 2D CNNs connected in cascade, specially designed to minimize false detections due to muscles, heart, lungs, and thoracic cavity. To identify deceptively enhancing regions, a 3D nMITR map of the segmented breast is computed and converted into binary form. During this process tissues that have low degrees of enhancements are discarded. To boost lesions, this binary image is processed by a 3D CNN with a control template consisting of three layers of 11 x 11 cells and a fuzzy c-partitioning output function. A set of decision rules extracted empirically from the training data set based on volume and 3D eccentricity features is used to make final decisions and localize lesions. The segmentation algorithm performs well with high average precision, high true positive volume fraction, and low false positive volume fraction with an overall performance of 0.93 +/- 0.05, 0.96 +/- 0.04, and 0.03 +/- 0.05, respectively (training: 0.93 +/- 0.04, 0.94 +/- 0.04, and 0.02 +/- 0.03; test: 0.93 +/- 0.05, 0.97 +/- 0.03, and 0.05 +/- 0.06). The lesion detection performance of the system is quite satisfactory; for the training data set the maximum detection sensitivity is 100% with false-positive detections of 0.28/lesion, 0.09/slice, and 0.65/case; for the test data set the maximum detection sensitivity is 97% with false-positive detections of 0.43/lesion, 0.11/slice, and 0.68/case. On the average, for a detection sensitivity of 99%, the overall performance of the system is 0.34/lesion, 0.10/slice, and 0.67/case. The system introduced does not require prior information concerning breast anatomy; it is robust and exceptionally effective for detecting breast lesions. The use of CNNs, fuzzy c-partitioning, volume, and 3D eccentricity criteria reduces false-positive detections due to artifacts caused by highly enhanced blood vessels, nipples, and normal parenchyma and artifacts from vascularized tissues in the chest wall due to oversegmentation. We hope that this system will facilitate breast examinations, improve the localization of lesions, and reduce unnecessary mastectomies, especially due to missed multicentric lesions and that almost real-time processing speeds achievable by direct hardware implementations will open up new clinical applications, such as making feasible quasi-automated MR-guided biopsies and acquisition of additional postcontrast lesion images to improve morphological characterizations.