388 research outputs found
Model-Based Visualization for Intervention Planning
Computer support for intervention planning is often a two-stage process: In a first stage, the relevant segmentation target structures are identified and delineated. In a second stage, image analysis results are employed for the actual planning process. In the first stage, model-based segmentation techniques are often used to reduce the interaction effort and increase the reproducibility. There is a similar argument to employ model-based techniques for the visualization as well. With increasingly more visualization options, users have many parameters to adjust in order to generate expressive visualizations. Surface models may be smoothed with a variety of techniques and parameters. Surface visualization and illustrative rendering techniques are controlled by a large set of additional parameters. Although interactive 3d visualizations should be flexible and support individual planning tasks, appropriate selection of visualization techniques and presets for their parameters is needed. In this chapter, we discuss this kind of visualization support. We refer to model-based visualization to denote the selection and parameterization of visualization techniques based on \u27a priori knowledge concerning visual perception, shapes of anatomical objects and intervention planning tasks
Reducing artifacts in surface meshes extracted from binary volumes
We present a mesh filtering method for surfaces extracted from binary volume data which guarantees a smooth
and correct representation of the original binary sampled surface, even if the original volume data is inaccessible
or unknown. This method reduces the typical block and staircase artifacts but adheres to the underlying binary
volume data yielding an accurate and smooth representation. The proposed method is closest to the technique of
Constrained Elastic Surface Nets (CESN). CESN is a specialized surface extraction method with a subsequent
iterative smoothing process, which uses the binary input data as a set of constraints. In contrast to CESN, our
method processes surface meshes extracted by means of Marching Cubes and does not require the binary volume.
It acts directly and solely on the surface mesh and is thus feasible even for surface meshes of inaccessible
or unknown volume data. This is possible by reconstructing information concerning the binary volume from
artifacts in the extracted mesh and applying a relaxation method constrained to the reconstructed information
Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splits
Purpose: The development of machine learning models for surgical workflow and
instrument recognition from temporal data represents a challenging task due to
the complex nature of surgical workflows. In particular, the imbalanced
distribution of data is one of the major challenges in the domain of surgical
workflow recognition. In order to obtain meaningful results, careful
partitioning of data into training, validation, and test sets, as well as the
selection of suitable evaluation metrics are crucial. Methods: In this work, we
present an openly available web-based application that enables interactive
exploration of dataset partitions. The proposed visual framework facilitates
the assessment of dataset splits for surgical workflow recognition, especially
with regard to identifying sub-optimal dataset splits. Currently, it supports
visualization of surgical phase and instrument annotations. Results: In order
to validate the dedicated interactive visualizations, we use a dataset split of
the Cholec80 dataset. This dataset split was specifically selected to reflect a
case of strong data imbalance. Using our software, we were able to identify
phases, phase transitions, and combinations of surgical instruments that were
not represented in one of the sets. Conclusion: In order to obtain meaningful
results in highly unbalanced class distributions, special care should be taken
with respect to the selection of an appropriate split. Interactive data
visualization represents a promising approach for the assessment of machine
learning datasets. The source code is available at
https://github.com/Cardio-AI/endovis-mlComment: Accepted at the 14th International Conference on Information
Processing in Computer-Assisted Interventions (IPCAI 2023); 9 pages, 4
figures, 1 tabl
Ten Open Challenges in Medical Visualization
The medical domain has been an inspiring application area in visualization research for many years already, but many open challenges remain. The driving forces of medical visualization research have been strengthened by novel developments, for example, in deep learning, the advent of affordable VR technology, and the need to provide medical visualizations for broader audiences. At IEEE VIS 2020, we hosted an Application Spotlight session to highlight recent medical visualization research topics. With this article, we provide the visualization community with ten such open challenges, primarily focused on challenges related to the visualization of medical imaging data. We first describe the unique nature of medical data in terms of data preparation, access, and standardization. Subsequently, we cover open visualization research challenges related to uncertainty, multimodal and multiscale approaches, and evaluation. Finally, we emphasize challenges related to users focusing on explainable AI, immersive visualization, P4 medicine, and narrative visualization.acceptedVersio
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