Multi-faceted Visual Analysis of Inter-Observer Variability

Information

• Publication Type: Master Thesis
• Workgroup(s)/Project(s): not specified
• Date: 2022
• Date (Start): March 2022
• Date (End): December 2022
• Second Supervisor: Renata Raidou
• Diploma Examination: 16. January 2023
• Open Access: yes
• First Supervisor: Manuela Waldner
• Pages: 145
• Keywords: medical visualization, uncertainty, inter-observer variability, data acquisition, segmentation, manual delineation

Abstract

Despite the advancements in auto-segmentation tools, manual delineation is still necessary in the medical field. For example, tumor segmentation is a crucial step in cancer radiotherapy and is still widely performed by hand by experienced radiologists. However, the opinions of experienced radiologists might differ, for a multitude of reasons. In this work, we visualize the variability originating from multiple experts delineating medical scans of the same patient, known as inter-observer variability.The novelty of this work consists of capturing the process of segmenting a target object. The focus lies in gaining insight into the observer’s thought processes and reasoning strategies. To investigate these aspects of segmenting we conduct a data acquisitionwith novice users and experts, capturing their thoughts in a think-aloud protocol and their areas of attention by tracking their mouse-movement during the segmentation process. This data is visualized with our Multi Observer Looking Environment (MOLE).MOLE allows to gain deep insight into the observers’ segmentation process and enables to compare different segmentation outcomes and how these occurred. With our proposed visualization techniques we emphasize regions of uncertainty that need more attention when delineating. Additionally, relevant keywords are extracted from the think-aloud protocol and aligned with the positions in the segmentation, providing information about the thought process of an observer. We link the initial image to a three-dimensional representation of the delineations and provide more details of the think-aloud protocol on demand.Our approach is universal to segmentation, attention and thought process data regardless of the domain of the data. We show how MOLE can be used with a medical dataset as well as an artificially created dataset. By validating our approach with the help of a medical expert actively working in the field, we define potential use cases in the existing pipeline of tumor delineation for cancer treatment.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.34726/hss.2022.105180

BibTeX

@mastersthesis{bayat-2022-mva,
  title =      "Multi-faceted Visual Analysis of Inter-Observer Variability",
  author =     "Hannah Bayat",
  year =       "2022",
  abstract =   "Despite the advancements in auto-segmentation tools, manual
               delineation is still necessary in the medical field. For
               example, tumor segmentation is a crucial step in cancer
               radiotherapy and is still widely performed by hand by
               experienced radiologists. However, the opinions of
               experienced radiologists might differ, for a multitude of
               reasons. In this work, we visualize the variability
               originating from multiple experts delineating medical scans
               of the same patient, known as inter-observer variability.The
               novelty of this work consists of capturing the process of
               segmenting a target object. The focus lies in gaining
               insight into the observer’s thought processes and
               reasoning strategies. To investigate these aspects of
               segmenting we conduct a data acquisitionwith novice users
               and experts, capturing their thoughts in a think-aloud
               protocol and their areas of attention by tracking their
               mouse-movement during the segmentation process. This data is
               visualized with our Multi Observer Looking Environment
               (MOLE).MOLE allows to gain deep insight into the
               observers’ segmentation process and enables to compare
               different segmentation outcomes and how these occurred. With
               our proposed visualization techniques we emphasize regions
               of uncertainty that need more attention when delineating.
               Additionally, relevant keywords are extracted from the
               think-aloud protocol and aligned with the positions in the
               segmentation, providing information about the thought
               process of an observer. We link the initial image to a
               three-dimensional representation of the delineations and
               provide more details of the think-aloud protocol on
               demand.Our approach is universal to segmentation, attention
               and thought process data regardless of the domain of the
               data. We show how MOLE can be used with a medical dataset as
               well as an artificially created dataset. By validating our
               approach with the help of a medical expert actively working
               in the field, we define potential use cases in the existing
               pipeline of tumor delineation for cancer treatment.",
  pages =      "145",
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien",
  keywords =   "medical visualization, uncertainty, inter-observer
               variability, data acquisition, segmentation, manual
               delineation",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2022/bayat-2022-mva/",
}