VCBM 2023: Eurographics Workshop on Visual Computing for Biology and Medicine

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https://diglib.eg.org/handle/10.2312/3543877

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Browsing VCBM 2023: Eurographics Workshop on Visual Computing for Biology and Medicine by Subject "Computing methodologies"

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    Bio-Sketch: A New Medium for Interactive Storytelling Illustrated by the Phenomenon of Infection
    (The Eurographics Association, 2023) Olivier, Pauline; Chabrier, Renaud; Memari, Pooran; Coll, Jean-Luc; Cani, Marie-Paule; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
    In the field of biology, digital illustrations play a crucial role in conveying complex phenomena, allowing for idealized shapes and motion, in contrast to data visualization. In the absence of suitable media, scientists often rely on oversimplified 2D figures or have to call in professional artists to create better illustrations, which can be limiting. We introduce Bio-Sketch, a novel progressive sketching system designed to ease the creation of animated illustrations, as exemplified here in the context of the infection phenomenon. Our solution relies on a new progressive sketching paradigm that seamlessly combines 3D modeling and pattern-based shape distribution to create background volume and temporal animation control. The elements created can be assembled into a complex scenario, enabling narrative design experiments for educational applications in biology. Our results and first feedback from experts in illustration and biology demonstrate the potential of Bio-Sketch to assist communication on the infection phenomenon, helping to bridge the gap between expert and non-expert audiences.
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    CDF-Based Importance Sampling and Visualization for Neural Network Training
    (The Eurographics Association, 2023) Knutsson, Alex; Unnebäck, Jakob; Jönsson, Daniel; Eilertsen, Gabriel; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
    Training a deep neural network is computationally expensive, but achieving the same network performance with less computation is possible if the training data is carefully chosen. However, selecting input samples during training is challenging as their true importance for the optimization is unknown. Furthermore, evaluation of the importance of individual samples must be computationally efficient and unbiased. In this paper, we present a new input data importance sampling strategy for reducing the training time of deep neural networks. We investigate different importance metrics that can be efficiently retrieved as they are available during training, i.e., the training loss and gradient norm. We found that choosing only samples with large loss or gradient norm, which are hard for the network to learn, is not optimal for the network performance. Instead, we introduce an importance sampling strategy that selects samples based on the cumulative distribution function of the loss and gradient norm, thereby making it more likely to choose hard samples while still including easy ones. The behavior of the proposed strategy is first analyzed on a synthetic dataset, and then evaluated in the application of classification of malignant cancer in digital pathology image patches. As pathology images contain many repetitive patterns, there could be significant gains in focusing on features that contribute stronger to the optimization. Finally, we show how the importance sampling process can be used to gain insights about the input data through visualization of samples that are found most or least useful for the training.
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    NeRF for 3D Reconstruction from X-ray Angiography: Possibilities and Limitations
    (The Eurographics Association, 2023) Maas, Kirsten W. H.; Pezzotti, Nicola; Vermeer, Amy J. E.; Ruijters, Danny; Vilanova, Anna; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
    Neural Radiance Field (NeRF) is a promising deep learning technique based on neural rendering for three-dimensional (3D) reconstruction. This technique has overcome several limitations of 3D reconstruction techniques, such as removing the need for 3D ground truth or two-dimensional (2D) segmentations. In the medical context, the 3D reconstruction of vessels from 2D X-ray angiography is a relevant problem. For example, the treatment of coronary arteries could still benefit from 3D reconstruction solutions, as common solutions do not suffice. Challenging areas in the 3D reconstruction from X-ray angiography are the vessel morphology characteristics, such as sparsity, overlap, and the distinction between foreground and background. Moreover, sparse view and limited angle X-ray projections restrict the information available for the 3D reconstructions. Many traditional and machine learning methods have been proposed, but they rely on demanding user interactions or require large amounts of training data. NeRF could solve these limitations, given that promising results have been shown for medical (X-ray) applications. However, to the best of our knowledge, no results have been shown with X-ray angiography projections or consider the vessel morphology characteristics. This paper explores the possibilities and limitations of using NeRF for 3D reconstruction from X-ray angiography. An extensive experimental analysis is conducted to quantitatively and qualitatively evaluate the effects of the X-ray angiographic challenges on the reconstruction quality. We demonstrate that NeRF has the potential for 3D Xray angiography reconstruction (e.g., reconstruction with sparse and limited angle X-ray projections) but also identify explicit limitations (e.g., the overlap of background structures) that must be addressed in future works.
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    Neural Deformable Cone Beam CT
    (The Eurographics Association, 2023) Birklein, Lukas; Schömer, Elmar; Brylka, Robert; Schwanecke, Ulrich; Schulze, Ralf; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
    In oral and maxillofacial cone beam computed tomography (CBCT), patient motion is frequently observed and, if not accounted for, can severely affect the usability of the acquired images. We propose a highly flexible, data driven motion correction and reconstruction method which combines neural inverse rendering in a CBCT setting with a neural deformation field. We jointly optimize a lightweight coordinate based representation of the 3D volume together with a deformation network. This allows our method to generate high quality results while accurately representing occurring patient movements, such as head movements, separate jaw movements or swallowing. We evaluate our method in synthetic and clinical scenarios and are able to produce artefact-free reconstructions even in the presence of severe motion. While our approach is primarily developed for maxillofacial applications, we do not restrict the deformation field to certain kinds of motion. We demonstrate its flexibility by applying it to other scenarios, such as 4D lung scans or industrial tomography settings, achieving state-of-the art results within minutes with only minimal adjustments.
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    Resectograms: Real-Time 2D Visualization of Liver Virtual Resections
    (The Eurographics Association, 2023) Meng, Ruoyan; Aghayan, Davit; Pelanis, Egidijus; Edwin, Bjørn; Cheikh, Faouzi Alaya; Palomar, Rafael; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
    Visualization of virtual resections plays a central role in computer-assisted liver surgery planning. The complexity of the liver's internal structures often leads to difficulties in its proper visualization during the positioning of virtual resections. Occlusions by vessels and tumors are common problems leading to non-preservation of resection margin, incorrect intersection with vessels, and resections. To overcome these challenges, we introduce Resectograms: a visualization approach based on 2D representations of virtual resections, which enable the visualization of information associated with surgical planning. These representations are presented as an additional 2D view displaying anatomical, functional, and risk-associated information extracted from the virtual resection in real-time. This view offers surgeons a simple and occlusion-free visualization of the virtual resection during surgical planning. Our pilot experiment with clinicians shows that the use of this visualization tool provides more information while planning virtual resections and has the potential to enhance confidence in accurate resection. The code repository and supplementary materials for this work is available at: https://github.com/ALive-research/Slicer-Liver
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    Visual Analytics to Support Treatment Decisions in Late-Stage Melanoma Patients
    (The Eurographics Association, 2023) Pereira, Calida; Niemann, Uli; Braun, Andreas; Mengoni, Miriam; Tüting, Thomas; Preim, Bernhard; Meuschke, Monique; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
    We present a visual analytics system to support treatment decisions in late-stage Melanoma patients. With the aim of improving patient outcomes, personalized treatment decisions based on individual characteristics and medical histories are crucial. The research focuses on the design and development of a visual analytics system tailored specifically for tumor boards, where multidisciplinary teams collaborate to make informed decisions. By leveraging a comprehensive database containing treatment and tumor stage progression information from over 1100 patients, the system provides healthcare professionals with a holistic overview and facilitates the analysis of individual cases as well as comparisons between multiple patients. The distinction between tumor board preparation systems and systems used during discussions is emphasized to ensure user-centric design and usability. Through the use of visual analytics techniques, complex relationships between treatment outcomes, temporal features, and patient-specific factors are explored, enabling clinicians to identify patterns and trends that may impact treatment decisions. The findings of this research contribute to the growing field of visual analytics in healthcare and have the potential to enhance treatment decision-making and patient care in late-stage cancer scenarios.