Purpose: The purpose of this study was to develop a robust breast-region segmentation method independent from the visible contrast between the breast region and surrounding chest wall and skin. Materials and methods: A fully-automated method for segmentation of the breast region in the axial MR images is presented relying on the edge map (EM) obtained by applying a tunable Gabor filter which sets its parameters according to the local MR image characteristics to detect non-visible transitions between different tissues having a similar MRI signal intensity. The method applies the shortest-path search technique by incorporating a novel cost function using the EM information within the border-search area obtained based on the border information from the adjacent slice. It is validated on 52 MRI scans covering the full American College of Radiology Breast Imaging-Reporting and Data System (BI-RADS) breast-density range. Results: The obtained results indicate that the method is robust and applicable for the challenging cases where a part of the fibroglandular tissue is connected to the chest wall and/or skin with no visible contrast, i.e. no fat presence, between them compared to the literature methods proposed for the axial MR images. The overall agreement between automatically- and manually-obtained breast-region segmentations is 96.1% in terms of the Dice Similarity Coefficient, and for the breast-chest wall and breast-skin border delineations it is 1.9 mm and 1.2 mm, respectively, in terms of the Mean-Deviation Distance. Conclusion: The accuracy, robustness and applicability for the challenging cases of the proposed method show its potential to be incorporated into computer-aided analysis systems to support physicians in their decision making.
COBISS.SI-ID: 28621095
The paper presents a computer-based assessment for facioscapulohumeral dystrophy (FSHD) diagnosis through characterisation of the fat and oedema percentages in the muscle region. A novel multi-slice method for the muscle-region segmentation in the T1-weighted magnetic resonance images is proposed using principles of the live-wire technique to find the path representing the muscle-region border. For this purpose, an exponential cost function is used that incorporates the edge information obtained after applying the edge-enhancement algorithm formerly designed for the fingerprint enhancement. The difference between the automatic segmentation and manual segmentation performed by a medical specialists is characterised using the Zijdenbos similarity index, indicating a high accuracy of the proposed method. Finally, the fat and oedema are quantified from the muscle region in the T1-weighted and T2-STIR magnetic resonance images, respectively, using the fuzzy c-mean clustering approach for 10 FSHD patients.
COBISS.SI-ID: 28621351
To investigate the feasibility of magnetic resonance (MR) electric impedance tomography (EIT) technique for in situ monitoring of electric field distribution during in vivo electroporation of mouse tumors to predict reversibly electroporated tumor areas. Materials and Methods All experiments received institutional animal care and use committee approval. Group 1 consisted of eight tumors that were used for determination of predicted area of reversibly electroporated tumor cells with MR EIT by using a 2.35-T MR imager. In addition, T1-weighted images of tumors were acquired to determine entrapment of contrast agent within the reversibly electroporated area. A correlation between predicted reversible electroporated tumor areas as determined with MR EIT and areas of entrapped MR contrast agent was evaluated to verify the accuracy of the prediction. Group 2 consisted of seven tumors that were used for validation of radiologic imaging with histopathologic staining. Histologic analysis results were then compared with predicted reversible electroporated tumor areas from group 1. Results were analyzed with Pearson correlation analysis and one-way analysis of variance. Results Mean coverage % standard deviation of tumors with electric field that leads to reversible electroporation of tumor cells obtained with MR EIT (38% % 9) and mean fraction of tumors with entrapped MR contrast agent (41% % 13) were correlated (Pearson analysis, r = 0.956, P = .005) and were not statistically different (analysis of variance, P = .11) from mean fraction of tumors from group 2 with entrapped fluorescent dye (39% % 12). Conclusion MR EIT can be used for determining electric field distribution in situ during electroporation of tissue. Implementation of MR EIT in electroporation-based applications, such as electrochemotherapy and irreversible electroporation tissue ablation, would enable corrective interventions before the end of the procedure and would additionally improve the treatment outcome.
COBISS.SI-ID: 10729556