MaInteractiveSeg

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

Interactive Segmentation for Improving Infection Quantification in CT scans

Supervision: Prof. Dr. Nassir Navab, Dr. Seong Tae Kim

Abstract

Longitudinal changes of pathologies in CT images is an important indicator for analyzing patients from COVID19. To accurately analyze changes of pathologies, consistent segmentation across multiple time-points is required. Only a few studies have been reported for COVID inspection segmentation but there is no study on longitudinal data [1-3]. Moreover, it is very challenging to segment multiple pathologies due to the inter-class similarity and intra-class variability. To address these issues, in this project, we will explore a novel method to fully exploit user guidance. We consider two types of user-guided interactive segmentation. 1) The user provides a pathology mask (segmentation mask, line, circle, scribbles, etc.) on the reference scan. In this scenario, the longitudinal segmentation model will be designed to use the reference scan’s mask as additional input for the segmentation as in [4-5]. By utilizing the information on the target pathology from the user’s input, the network focuses on the target class, which makes the problem easier. 2) The user indicates erroneous areas. Then, the network will utilize the information to refine the segmentation [6-8]. We would explore this interactive segmentation on the longitudinal Covid-19 segmentation problem.

Requirements:

  • Good understanding of statistics and machine learning methods.
  • Very good programming skills in Python & TensorFlow? / PyTorch?

Location:

  • Garching

ProjectForm
Title: Interactive Segmentation for Improving Infection Quantification in CT scans
Abstract: Longitudinal changes of pathologies in CT images is an important indicator for analyzing patients from COVID19. To accurately analyze changes of pathologies, consistent segmentation across multiple time-points is required. Only a few studies have been reported for COVID inspection segmentation but there is no study on longitudinal data [1-3]. Moreover, it is very challenging to segment multiple pathologies due to the inter-class similarity and intra-class variability. To address these issues, in this project, we will explore a novel method to fully exploit user guidance. We consider two types of user-guided interactive segmentation. 1) The user provides a pathology mask (segmentation mask, line, circle, scribbles, etc.) on the reference scan. In this scenario, the longitudinal segmentation model will be designed to use the reference scan’s mask as additional input for the segmentation as in [4-5]. By utilizing the information on the target pathology from the user’s input, the network focuses on the target class, which makes the problem easier. 2) The user indicates erroneous areas. Then, the network will utilize the information to refine the segmentation [6-8]. We would explore this interactive segmentation on the longitudinal Covid-19 segmentation problem.
Student:  
Director: Prof. Dr. Nassir Navab
Supervisor: Dr. Seong Tae Kim
Type: Master Thesis
Area: Segmentation, Machine Learning, Medical Imaging
Status: running
Start:  
Finish:  
Thesis (optional):  
Picture:  


Edit | Attach | Refresh | Diffs | More | Revision r1.3 - 19 Oct 2020 - 11:13 - SeongTaeKim