@inproceedings{47e5da1177384d2fa413761c8af99d27,
title = "Generic User-guided interaction paradigm for precise post-slice-wise processing of tomographic deep learning segmentations utilizing graph cut and graph segmentation",
abstract = "State of the art deep learning (DL) manifested in image processing as an accurate segmentation method. Nevertheless, its black-box nature hardly allows user interference. In this paper, we present a generic Graph cut (GC) and Graph segmentation (GS) approach for user-guided interactive post-processing of segmentations resulting from DL. The GC fitness function incorporates both, the original image characteristics and DL segmentation results, combining them with weights optimized by evolution strategy optimization. To allow for accurate user-guided processing, the fore- and background seeds of the Graph cut are automatically selected from the DL segmentations, but implementing effective features for expert input for adaptions of position and topology. The seamless integration of DL with GC/GS leads to marginal trade-off in quality, namely Jaccard (JI) 1.3% for automated GC and JI 0.46% for GS only. Yet, in specific areas where a welltrained DL model may potentially fail, precise adaptions at a low demand for user-interaction become feasible and thus even outperforming the original DL results. The potential of GC/GS is shown running on groundtruth seeds thereby outperforming DL by 0.44% JI for the GC and even by 1.16% JI for the GS. Iterative sliceby- slice progression of the post-processed and improved results keeps the demand for user-interaction low.",
keywords = "Deep Learning Image Segmentation, Evolution-strategy, Graph Cut, Graph Segmentation, U-Net, User-guided Medical Image Analysis",
author = "Zwettler, {Gerald A.} and Werner Backfrieder and Karwoski, {Ronald A.} and Holmes, {David R.}",
note = "Publisher Copyright: Copyright {\textcopyright} 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.; 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021 ; Conference date: 08-02-2021 Through 10-02-2021",
year = "2021",
language = "English (US)",
series = "VISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications",
publisher = "SciTePress",
pages = "235--244",
editor = "Farinella, {Giovanni Maria} and Petia Radeva and Jose Braz and Kadi Bouatouch",
booktitle = "VISAPP",
}