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Techn. Fakultät Willkommen am Institut für Informatik FAU-Logo

Sulaiman Vesal M. Sc.

Researcher in the Learning Approaches for Medical Big Data Analysis (LAMBDA) group at the Pattern Recognition Lab of the Friedrich-Alexander-Universität Erlangen-Nürnberg

Contacts

Room: 10.136

Telephone: +49 1521 02 28519

E-mail: Sulaiman.vesal(at)fau.de

Research Interests

  • Multi-modality breast image analysis and fusion
  • Computer-aided detection and diagnosis of breast cancer
  • Lesion classification in 3D MRI
  • Quantitative analysis of breast imaging modalities
  • Large-scale breast image screening and analysis

Semi-Automatic Algorithm for Breast MRI Lesion Segmentation Using Marker-Controlled Watershed Transformation
Sulaiman Vesal, Andres Diaz-Pinto, Nishant Ravikumar, Stephan Ellmann, Amirabbas Davari and Andreas Maier

Magnetic resonance imaging (MRI) is an effective imaging modality for identifying and localizing breast lesions in women. Accurate and precise lesion segmentation using a computer-aided-diagnosis (CAD) system, is a crucial step in evaluating tumor volume and in the quantification of tumor characteristics. However, this is a challenging task, since breast lesions have sophisticated shape, topological structure, and high variance in their intensity distribution across patients. We proposed a novel marker-controlled watershed transformation-based approach, which uses the brightest pixels in a region of interest (determined by experts) as markers to overcome this challenge, and accurately segment lesions in breast MRI. The proposed approach was evaluated on 106 lesions, which includes 64 malignant and 42 benign cases.  

The results illustrate that the proposed method shows promise for future work related to the segmentation and classification of benign and malignant breast lesions.

Classification of breast cancer histology images using transfer learning
Sulaiman Vesal, Nishant Ravikumar, AmirAbbas Davari, Stephan Ellmann, Andreas Maier

 Breast cancer is one of the leading causes of mortality in women. Early detection and treatment are imperative for improving survival rates, which have steadily increased in recent years as a result of more sophisticated computer-aided-diagnosis (CAD) systems. CAD systems are essential to reduce subjectivity and supplement the analyses conducted by specialists. We propose a transfer learning-based approach, for the task of breast histology image classification into four tissue sub-types, namely, normal, benign, Insitu carcinoma and invasive carcinoma. The histology images, provided as part of the BACH 2018 grand challenge, were first normalized to correct for color variations induced during slide preparation. Subsequently, image patches were extracted and used to fine-tune Google`s Inception-V3 and ResNet50 convolutional neural networks, both pre-trained on the ImageNet database, enabling them to learn domain-specific features, necessary to classify the histology images. Classification accuracy was evaluated using 3-folds. The Inception-V3 network achieved an average test accuracy of 97.08\% for four classes, marginally outperforming the ResNet50 network, which achieved an average accuracy of 96.66\%.

Breast DCE-MRI Lesion Segmentation and Classification

Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) recently proved to be quite helpful in screening high-risk women and in staging newly diagnosed breast cancer patients. Accurate segmentation of tumor lesion and selection of suspicious regions of interest (ROIs) is a critical pre-processing step in DCE-MRI data evaluation. The goal of this project is to develop and evaluate methods for automatic and semi-automatic detection of suspicious ROIs for breast DCE-MRI, segmentation of tumor lesions, morphological feature extraction and classification of tumors in terms of benign and malignant. I will try to use deep learning algorithms as state-of-the-art approaches for every possible stages. This project is partly connected to Big-Thera project and the patients data are provided by Radiology department of Killinikum University of Erlangen.