Research project

Development of multi-modal, multi-scale imaging framework for the early diagnosis of breast cancer

Breast cancer is the leading cause of cancer related deaths in women, the second most common cancer worldwide. The development and progression of breast cancer is a dynamic biological and evolutionary process. It involves a composite organ system, with transcriptome shaped by gene aberrations, epigenetic changes, the cellular biological context, and environmental influences. Breast cancer growth and response to treatment has a number of characteristics that are specific to the individual patient, for example the response of the immune system and the interaction with the neighboring tissue. The overall complexity of breast cancer is the main cause for the current, unsatisfying understanding of its development and the patient’s therapy response. Although recent precision medicine approaches, including genomic characterization and immunotherapies, have shown clear improvements with regard to prognosis, the right treatment of this disease remains a serious challenge. The vision of the BIG-THERA team is to improve individualized breast cancer diagnostics and therapy, with the ultimate goal of extending the life expectancy of breast cancer sufferers. Our primary contribution in this regard is developing a multi-modal, multi-scale framework for the early diagnosis of the molecular sub-types of breast cancer, in a manner that supplements the clinical diagnostic workflow and enables the early identification of patients compatible with specific immunotherapeutic solutions.

Project manager:
Prof. Dr.-Ing. habil. Andreas Maier

Project participants:
Sulaiman Vesal, M. Sc., Nishant Ravikumar, Ph.D.

Keywords:
Computer-Aided Diagnosis; Beast Cancer; Deep Learning; Breast Multi-modality Imaging; Breast Lesion Detection, Breast Malignancy Classification

Duration: 1.1.2017 - 30.9.2019

Sponsored by:
FAU Emerging Fields Initiative (EFI)

Contact:
Vesal, Sulaiman
Phone +49 9131 85 27799, Fax +49 9131 85 27270, E-Mail: sulaiman.vesal@fau.de

Publications

Vesal, Sulaiman ; Malakarjun Patil, Shreyas ; Ravikumar, Nishant ; Maier, Andreas: A Multi-task Framework for Skin Lesion Detection and Segmentation. In: Springer International Publishing (Ed.) : OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis (International Workshop on Skin Image Analysis(ISIC 2018, Held in Conjunction with MICCAI 2018) Granada, Spain 20.09.2018). 2018, pp 285-293. - ISBN 978-3-030-01201-4
[doi>10.1007/978-3-030-01201-4_31]

Vesal, Sulaiman ; Ravikumar, Nishant ; Davari, AmirAbbas ; Ellmann, Stephan ; Maier, Andreas: Classification of breast cancer histology images using transfer learning. In: Springer International Publishing (Ed.) : Image Analysis and Recognition (15th International Conference on Image Analysis and Recognition Póvoa de Varzim, Portugal 27-29.06.2018). 2018, pp 812-819. - ISBN 978-3-319-93000-8

Vesal, Sulaiman ; Ravikumar, Nishant ; Ellmann, Stephan ; Maier, Andreas: Comparative Analysis of Unsupervised Algorithms for Breast MRI Lesion Segmentation. In: Springer Berlin Heidelberg (Ed.) : Bildverarbeitung für die Medizin 2018 (Bildverarbeitung für die Medizin Erlangen, Germany 13.03). 2018, pp 257-262.

Vesal, Sulaiman ; Ravikumar, Nishant ; Maier, Andreas: Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI. In: Springer International Publishing (Ed.) : Statistical Atlases and Computational Modelling of the Heart (Statistical Atlases and Computational Modelling of the Heart Workshop (STACOM 2018, Held in conjunction with MICCAI 2018) Granada, Spain 16.08.2018). 2018, pp tbd.

Ellmann, Stephan ; Bielowski, Christian ; Wenkel, Evelyn ; Vesal, Sulaiman ; Maier, Andreas ; Uder, Michael ; Bäuerle, Tobias: Ein klinisch anwendbares Neuronales Netzwerk zur Klassifikation suspekter Läsionen in der Mamma-MRT. In: Fortschr Röntgenstr 190 (2018), No. 0, pp WISS 401.2
[doi>10.1055/s-0038-1641379]

Vesal, Sulaiman ; Ravikumar, Nishant ; Maier, Andreas: SkinNet: A Deep Learning Framework for Skin Lesion Segmentation. In: IEEE (Ed.) : IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) (2018 IEEE Nuclear Science Symposium and Medical Imaging (NSS/MIC) Sydney, Australia 10-17.11.2018). 2018, pp tbd.

Vesal, Sulaiman ; Diaz-Pinto, Andres ; Ravikumar, Nishant ; Ellmann, Stephan ; Davari, AmirAbbas ; Maier, Andreas: Semi-Automatic Algorithm for Breast MRI Lesion Segmentation Using Marker-Controlled Watershed Transformation. In: IEEE (Ed.) : 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) (IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) Atlanta, Georgia, USA 21.10-28.10.2017). 2017, pp tbd.