no code implementations • 11 Dec 2023 • Negin Ghamsarian, Yosuf El-Shabrawi, Sahar Nasirihaghighi, Doris Putzgruber-Adamitsch, Martin Zinkernagel, Sebastian Wolf, Klaus Schoeffmann, Raphael Sznitman
Besides, we initiate the research on domain adaptation for instrument segmentation in cataract surgery by evaluating cross-domain instrument segmentation performance in cataract surgery videos.
no code implementations • 6 Dec 2023 • Negin Ghamsarian, Sebastian Wolf, Martin Zinkernagel, Klaus Schoeffmann, Raphael Sznitman
We propose a network architecture, DeepPyramid+, which addresses diverse challenges encountered in medical image and surgical video segmentation.
no code implementations • 6 Dec 2023 • Negin Ghamsarian, Doris Putzgruber-Adamitsch, Stephanie Sarny, Raphael Sznitman, Klaus Schoeffmann, Yosuf El-Shabrawi
The Pearson correlation and t-test results reveal significant correlations between lens unfolding delay and lens rotation and significant differences between the intra-operative rotations stability of four groups of lenses.
no code implementations • 1 Dec 2023 • Sahar Nasirihaghighi, Negin Ghamsarian, Heinrich Husslein, Klaus Schoeffmann
In this paper, we introduce a comprehensive dataset tailored for relevant event recognition in laparoscopic gynecology videos.
no code implementations • 30 Nov 2023 • Sahar Nasirihaghighi, Negin Ghamsarian, Daniela Stefanics, Klaus Schoeffmann, Heinrich Husslein
Action recognition is a prerequisite for many applications in laparoscopic video analysis including but not limited to surgical training, operation room planning, follow-up surgery preparation, post-operative surgical assessment, and surgical outcome estimation.
1 code implementation • 31 Jul 2023 • Negin Ghamsarian, Javier Gamazo Tejero, Pablo Márquez Neila, Sebastian Wolf, Martin Zinkernagel, Klaus Schoeffmann, Raphael Sznitman
However, the unreliability of pseudo labels can hinder the capability of self-training techniques to induce abstract representation from the unlabeled target dataset, especially in the case of large distribution gaps.
no code implementations • International Conference on Multimedia Modeling 2023 • S Lubos, Massimiliano Rubino, Christian Tautschnig, Markus Tautschnig, Boda Wen, Klaus Schoeffmann, Alexander Felfernig
This paper presents the first version of our video search system Perfect Match for the Video Browser Showdown 2023 competition.
1 code implementation • 4 Jul 2022 • Negin Ghamsarian, Mario Taschwer, Raphael Sznitman, Klaus Schoeffmann
Semantic segmentation in cataract surgery has a wide range of applications contributing to surgical outcome enhancement and clinical risk reduction.
no code implementations • 25 Sep 2021 • Negin Ghamsarian, Mario Taschwer, Doris Putzgruber-Adamitsch, Stephanie Sarny, Yosuf El-Shabrawi, Klaus Schoeffmann
Semantic segmentation in surgical videos is a prerequisite for a broad range of applications towards improving surgical outcomes and surgical video analysis.
no code implementations • 11 Sep 2021 • Negin Ghamsarian, Mario Taschwer, Klaus Schoeffmann
This paper proposes a semantic segmentation network termed as DeepPyram that can achieve superior performance in segmenting relevant objects in cataract surgery videos with varying issues.
1 code implementation • 2 Jul 2021 • Negin Ghamsarian, Mario Taschwer, Doris Putzgruber-Adamitsch, Stephanie Sarny, Yosuf El-Shabrawi, Klaus Schoeffmann
In particular, we propose (I) an end-to-end recurrent neural network to recognize the lens-implantation phase and (II) a novel semantic segmentation network to segment the lens and pupil after the implantation phase.
no code implementations • 29 Apr 2021 • Negin Ghamsarian, Mario Taschwer, Doris Putzgruber-Adamitsch, Stephanie Sarny, Klaus Schoeffmann
This module consists of four parallel recurrent CNNs being responsible to detect four relevant phases that have been defined with medical experts.
no code implementations • 23 Mar 2020 • Tobias Ross, Annika Reinke, Peter M. Full, Martin Wagner, Hannes Kenngott, Martin Apitz, Hellena Hempe, Diana Mindroc Filimon, Patrick Scholz, Thuy Nuong Tran, Pierangela Bruno, Pablo Arbeláez, Gui-Bin Bian, Sebastian Bodenstedt, Jon Lindström Bolmgren, Laura Bravo-Sánchez, Hua-Bin Chen, Cristina González, Dong Guo, Pål Halvorsen, Pheng-Ann Heng, Enes Hosgor, Zeng-Guang Hou, Fabian Isensee, Debesh Jha, Tingting Jiang, Yueming Jin, Kadir Kirtac, Sabrina Kletz, Stefan Leger, Zhixuan Li, Klaus H. Maier-Hein, Zhen-Liang Ni, Michael A. Riegler, Klaus Schoeffmann, Ruohua Shi, Stefanie Speidel, Michael Stenzel, Isabell Twick, Gutai Wang, Jiacheng Wang, Liansheng Wang, Lu Wang, Yu-Jie Zhang, Yan-Jie Zhou, Lei Zhu, Manuel Wiesenfarth, Annette Kopp-Schneider, Beat P. Müller-Stich, Lena Maier-Hein
The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data.