scholarly journals An image segmentation framework for extracting tumors from breast magnetic resonance images

2018 ◽  
Vol 11 (04) ◽  
pp. 1850014 ◽  
Author(s):  
Le Sun ◽  
Jinyuan He ◽  
Xiaoxia Yin ◽  
Yanchun Zhang ◽  
Jeon-Hor Chen ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Magnetic Resonance ◽  
Breast Cancer Diagnosis ◽  
Vital Role ◽  
Magnetic Resonance Images ◽  
Tumor Segmentation ◽  
Breast Magnetic Resonance ◽  
Magnetic Resonance Imaging Mri ◽  
Tumor Extraction ◽  
Disease Analysis

Magnetic resonance imaging (MRI) has been a prevalence technique for breast cancer diagnosis. Computer-aided detection and segmentation of lesions from MRIs plays a vital role for the MRI-based disease analysis. There are two main issues of the existing breast lesion segmentation techniques: requiring manual delineation of Regions of Interests (ROIs) as a step of initialization; and requiring a large amount of labeled images for model construction or parameter learning, while in real clinical or experimental settings, it is highly challenging to get sufficient labeled MRIs. To resolve these issues, this work proposes a semi-supervised method for breast tumor segmentation based on super voxel strategies. After image segmentation with advanced cluster techniques, we take a supervised learning step to classify the tumor and nontumor patches in order to automatically locate the tumor regions in an MRI. To obtain the optimal performance of tumor extraction, we take extensive experiments to learn parameters for tumor segmentation and classification, and design 225 classifiers corresponding to different parameter settings. We call the proposed method as Semi-supervised Tumor Segmentation (SSTS), and apply it to both mass and nonmass lesions. Experimental results show better performance of SSTS compared with five state-of-the-art methods.

scholarly journals Automated 2D Segmentation of Prostate in T2-weighted MRI Scans

2016 ◽  
Vol 12 (1) ◽  
pp. 53
Author(s):  
Justinas Jucevicius ◽  
Povilas Treigys ◽  
Jolita Bernataviciene ◽  
Ruta Briediene ◽  
Ieva Naruševiciute ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Magnetic Resonance ◽  
Automatic Segmentation ◽  
Magnetic Resonance Images ◽  
Mr Image ◽  
Prostate Segmentation ◽  
Image Appearance ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri ◽  
Mr Image Segmentation

The prostate cancer is the second most frequent tumor amongst men. Statistics shows that biopsy reveals only 70-80% clinical cancer cases. Multiparametric magnetic resonance imaging (MRI) technique comes to play and is used to help to determine the location to perform a biopsy. With the aim to automating the biopsy localization, prostate segmentation has to be performed in magnetic resonance images. Computer image analysis methods play the key role here. The problem of automated prostate magnetic resonance (MR) image segmentation is burdened by the fact that MRI signal intensity is not standardized: field of view and image appearance is for a large part determined by acquisition protocol, field strength, coil profile and scanner type. Authors overview the most recent Prostate MR image segmentation challenge results and provide insights on T2-weighted MRI scan images automated prostate segmentation problem by comparing the best obtained automatic segmentation algorithms and applying them to 2D prostate segmentation case. The most important benefit of this research will have medical doctors involved in the management of the cancer.

scholarly journals Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1415 ◽  
Author(s):  
Satoshi Takahashi ◽  
Masamichi Takahashi ◽  
Manabu Kinoshita ◽  
Mototaka Miyake ◽  
Risa Kawaguchi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Segmentation ◽  
Magnetic Resonance ◽  
Magnetic Resonance Images ◽  
Fine Tuning ◽  
Tumor Segmentation ◽  
Dice Coefficient ◽  
Test Portion ◽  
Japanese Cohort ◽  
Significant Difference

Machine learning models for automated magnetic resonance image segmentation may be useful in aiding glioma detection. However, the image differences among facilities cause performance degradation and impede detection. This study proposes a method to solve this issue. We used the data from the Multimodal Brain Tumor Image Segmentation Benchmark (BraTS) and the Japanese cohort (JC) datasets. Three models for tumor segmentation are developed. In our methodology, the BraTS and JC models are trained on the BraTS and JC datasets, respectively, whereas the fine-tuning models are developed from the BraTS model and fine-tuned using the JC dataset. Our results show that the Dice coefficient score of the JC model for the test portion of the JC dataset was 0.779 ± 0.137, whereas that of the BraTS model was lower (0.717 ± 0.207). The mean Dice coefficient score of the fine-tuning model was 0.769 ± 0.138. There was a significant difference between the BraTS and JC models (p < 0.0001) and the BraTS and fine-tuning models (p = 0.002); however, no significant difference between the JC and fine-tuning models (p = 0.673). As our fine-tuning method requires fewer than 20 cases, this method is useful even in a facility where the number of glioma cases is small.

scholarly journals Comparative Study of Digital Breast Tomosynthesis (DBT) with and without Ultrasound versus Breast Magnetic Resonance Imaging (MRI) in Detecting Breast Lesion

2022 ◽  
Vol 19 (2) ◽  
pp. 759
Author(s):  
Janice Hui Ling Goh ◽  
Toh Leong Tan ◽  
Suraya Aziz ◽  
Iqbal Hussain Rizuana
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Diagnostic Performance ◽  
Breast Lesion ◽  
Breast Magnetic Resonance Imaging ◽  
Digital Breast Tomosynthesis ◽  
Resonance Imaging ◽  
Breast Tomosynthesis ◽  
Breast Magnetic Resonance ◽  
Magnetic Resonance Imaging Mri

Digital breast tomosynthesis (DBT) is a fairly recent breast imaging technique invented to overcome the challenges of overlapping breast tissue. Ultrasonography (USG) was used as a complementary tool to DBT for the purpose of this study. Nonetheless, breast magnetic resonance imaging (MRI) remains the most sensitive tool to detect breast lesion. The purpose of this study was to evaluate diagnostic performance of DBT, with and without USG, versus breast MRI in correlation to histopathological examination (HPE). This was a retrospective study in a university hospital over a duration of 24 months. Findings were acquired from a formal report and were correlated with HPE. The sensitivity of DBT with or without USG was lower than MRI. However, the accuracy, specificity and PPV were raised with the aid of USG to equivalent or better than MRI. These three modalities showed statistically significant in correlation with HPE (p < 0.005, chi-squared). Generally, DBT alone has lower sensitivity as compared to MRI. However, it is reassuring that DBT + USG could significantly improve diagnostic performance to that comparable to MRI. In conclusion, results of this study are vital to centers which do not have MRI, as complementary ultrasound can accentuate diagnostic performance of DBT.

Magnetic resonance imaging and computerized tomography scanning of herpes simplex encephalitis

1987 ◽  
Vol 67 (4) ◽  
pp. 592-594 ◽  
Author(s):  
Eric W. Neils ◽  
Robert Lukin ◽  
Thomas A. Tomsick ◽  
John M. Tew
Keyword(s):  
Magnetic Resonance Imaging ◽  
Herpes Simplex ◽  
Magnetic Resonance ◽  
Computerized Tomography ◽  
Herpes Simplex Encephalitis ◽  
Ct Scanning ◽  
Magnetic Resonance Images ◽  
Resonance Imaging ◽  
Content Type ◽  
Magnetic Resonance Imaging Mri

✓ The authors present two cases of herpes simplex encephalitis (HSE) in which computerized tomography (CT) scanning and magnetic resonance imaging (MRI) were performed. They also review the literature on the use of these imaging modalities in cases of HSE. The striking changes noted in these cases on T2-weighted magnetic resonance images in comparison to the CT findings suggest that MRI will help speed recognition of nonhemorrhagic HSE abnormalities.

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