MaAutCLT

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Weakly-Supervised Liver Lesion Localisation and Classification with Spectral CT Data

Supervision: Prof. Dr. Nassir Navab, Shadi Albarqouni, Peter Noel

Abstract

The liver can develop a number of different lesions, which can be both benign or malignant. The correct diagnosis is crucial to plan further treatment for the patient. Medical experts can benefit from using spectral computed tomography (CT) when making their diagnosis. This technology provides additional information about the tissue and contrast agent compared to a conventional CT by measuring material specific absorption properties. Automated liver lesion localization is currently an active field of research, however, usually requires precise segmentation of the lesions for the training set. Since there are no publicly available spectral CT datasets at this point, weakly-supervised learning will be used in this thesis. The goal of this thesis is to first localize anomalies in the liver with a weakly-supervised convolutional neural network (CNN) and in a second step classify the lesions that were found by the first network. The impact of the spectral CT for the network localization and classification in comparison to conventional CT will be investigated.

Tasks:

- Build an automatic model for Tumor Liver Classification - Build weakly supervised Tumor Liver Localization -

Requirements:

  • Good understanding of medical physics
  • Good understanding of statistics and machine learning methods.
  • Very good programming skills in Python & TensorFlow?
  • Willingness to closely collaborate with physicians and experts in machine learning.

Location:

  • Institute for Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Ismaninger Straße 22, 81675 München

Literature

Resultant Paper


ProjectForm
Title: Weakly-Supervised Liver Lesion Localisation and Classification with Spectral CT Data
Abstract: The liver can develop a number of different lesions, which can be both benign or malignant. The correct diagnosis is crucial to plan further treatment for the patient. Medical experts can benefit from using spectral computed tomography (CT) when making their diagnosis. This technology provides additional information about the tissue and contrast agent compared to a conventional CT by measuring material specific absorption properties. Automated liver lesion localization is currently an active field of research, however, usually requires precise segmentation of the lesions for the training set. Since there are no publicly available spectral CT datasets at this point, weakly-supervised learning will be used in this thesis. The goal of this thesis is to first localize anomalies in the liver with a weakly-supervised convolutional neural network (CNN) and in a second step classify the lesions that were found by the first network. The impact of the spectral CT for the network localization and classification in comparison to conventional CT will be investigated.
Student: Julia Fokhul
Director: Prof. Dr. Nassir Navab
Supervisor: Shadi Albarqouni, Peter Noel
Type: Master Thesis
Area:  
Status: finished
Start:  
Finish:  
Thesis (optional):  
Picture:  


Edit | Attach | Refresh | Diffs | More | Revision r1.3 - 26 Mar 2018 - 18:40 - ShadiAlbarqouni