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Lookup NU author(s): Emeritus Professor Michael Taggart, Mona Albargothy
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/). Image segmentation models are often evaluated using measures of overlap and boundary deviation between a ground truth and a prediction. These measures do not indicate whether a prediction is an overestimation or underestimation of the ground truth. This contextual information is critical in medical imaging applications such as tumor detection where a model's tendency to overestimate a prediction would be preferred to avoid overlooking malignant cells. Spatial reasoning provides context on a model's segmentation performance in terms of its tendency to over- or underestimate a region of interest. Such context can highlight a model's decision-making trends and can be applied to inform targeted improvements. In this work, we provide a Python module1,2 that implements a model-agnostic spatial reasoning pipeline for the contextual evaluation of segmentation methods. We apply this pipeline to the output of the Segment Anything model on 3 electron microscopy (EM) datasets and demonstrate the meaningful inferences that can be made.
Author(s): Porter V, Styles I, Curtis TM, Taggart MJ, Albargothy MJ, Gault R
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 26th Irish Machine Vision and Image Processing Conference (IMVIP 2024)
Year of Conference: 2024
Pages: 266-274
Online publication date: 25/09/2024
Acceptance date: 02/04/2018
Date deposited: 18/02/2025
ISSN: 2732-4494
Publisher: Institution of Engineering and Technology
URL: https://doi.org/10.1049/icp.2024.3314
DOI: 10.1049/icp.2024.3314
Series Title: IET Conference Proceedings
