Challenges and approaches to interactive visualization in healthcare workspaces

Three-dimensional (3D) medical images are prone to overlap, and there are some problems, such as low detection efficiency and inconsistent with the actual situation. Therefore, a 3D medical image surface reconstruction method based on data mining and machine learning is proposed. The 3D medical images were classified according to different ways, the information frame of 3D medical images was established and the surface overlapping information model of 3D images was given. Based on this information framework, the nonlinear function of overlapping area information of 3D medical images was constructed. The weight of the nonlinear function was used to calculate the input and output results of overlapping area information. Combined with the input mode of 3D medical image information, the error between the information output and the expected output was set. The nonlinear function weight of the overlapping area information of 3D medical images was modified by using the learning rate and the use time of the overlapping area information, and the influence factors of the overlapping information detection were obtained by increasing the situation terms, so as to complete the detection of the surface reconstruction information of 3D medical images. The experimental results show that the information detection results of the proposed method fit well with the actual situation, and the information detection efficiency is high.

The current state of the art in three dimensional oncologic imaging: An overview

Chapter 4 - 4D medical image analysis: a systematic study on applications, challenges, and future research directions

Interactive visualization of multiresolution image stacks in 3D

Three-dimensional (3D) medical images, such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), are essential for clinical applications. However, the need for diverse and comprehensive representations is particularly pronounced when considering the variability across different organs, diagnostic tasks, and imaging modalities. How to effectively interpret the intricate contextual information and extract meaningful insights from these images remains an open challenge to the community. While current self-supervised learning methods have shown potential, they often consider an image as a whole thereby overlooking the extensive, complex relationships among local regions from one or multiple images. In this work, we introduce a pioneering method for learning 3D medical image representations through an autoregressive pre-training framework. Our approach sequences various 3D medical images based on spatial, contrast, and semantic correlations, treating them as interconnected visual tokens within a token sequence. By employing an autoregressive sequence modeling task, we predict the next visual token in the sequence, which allows our model to deeply understand and integrate the contextual information inherent in 3D medical images. Additionally, we implement a random startup strategy to avoid overestimating token relationships and to enhance the robustness of learning. The effectiveness of our approach is demonstrated by the superior performance over others on nine downstream tasks in public datasets.

With its abrupt onset, stroke inevitably leads to a sudden change in life for the person surviving a stroke and their family. Having a stroke includes, not only a process of rehabilitation and recovery but also a process of making sense of and adapting to a new reality. Even though stroke is an acute condition its long-term complications and disability require a complex recovery process and dependency on support from the healthcare system. The recovery and sensemaking process often includes the need of involvement from a range of interventions and support services that requires coordination of multiple care providers and intersectoral collaboration within the healthcare delivery system. Unfortunately, healthcare systems are struggling with fragmentation and poor coordination of care and rehabilitation to ensure adequate quality post stroke care and support based on stroke survivors and their family’s needs. The overall aim of this project is to develop a continuous learning network for stroke care and rehabilitation to promote collaborative learning of stroke survivors, their families, healthcare professionals, managers, and policy makers. The project will use sensemaking theory, a participatory action research approach and co-design to develop a collaborative and interactive platform with the ultimate goal of promoting a more person-centred and integrated care for stroke survivors and their families. To achieve the overall aim the project has five objectives: Understanding how the current stroke care trajectory is experienced from the perspective of healthcare professionals, managers and policy makers and how they make sense of their current role, situation and context. Explore the needs of stroke survivors and their families and how they make sense of their new situation post stroke and how it changes over time. Identify facilitators and barriers towards developing a continuous learning network. Engage the stakeholders in a co-design process to ensure that their experiences, needs and preferences is reflected in the design of the network. Develop a prototype of an interactive digital platform for a continuous learning network. The project will contribute with knowledge of current networks and how the current stroke care trajectory is experienced from the perspective of stroke survivors and their families, healthcare professionals, managers and policymakers and how these stakeholders make sense of their current role, situation and situation and context. The continuous learning network will connect stroke survivors and their families, healthcare professionals and organizations for the purpose of sharing knowledge, best practices, and innovations. The network allows for the exchange of information and ideas, the identification of emerging trends, and co-creation of more sustainable solutions to complex healthcare challenges. The project will further provide learning on how sensemaking theory and participatory action research can be used to promote continuous learning, collaboration and knowledge exchange between the involved stakeholder to promote the implementation of a more person-centred integrated care. The long-term goal of the continuous learning network is to improve patient experiences and outcomes through better healthcare delivery and by enhancing the knowledge and skills of healthcare providers.

Three-dimensional (3D) medical image segmentation is used to segment the target (a lesion or an organ) in 3D medical images. Through this process, 3D target information is obtained; hence, this technology is an important auxiliary tool for medical diagnosis. Although some methods have proved to be successful for two-dimensional (2D) image segmentation, their direct use in the 3D case has been unsatisfactory. To obtain more precise tumor segmentation results from 3D MR images, in this paper, we propose a method known as the 3D shape-weighted level set method (3D-SLSM). The proposed method first converts the LSM, which is superior with respect to 2D image segmentation, into a 3D algorithm that is suitable for overall calculations in 3D image models, and which improves the efficiency and accuracy of calculations. A 3D shape-weighted value is then added for each 3D-SLSM iterative process according to the changes in volume. Besides increasing the convergence rate and eliminating background noise, this shape-weighted value also brings the segmented contour closer to the actual tumor margins. To perform a quantitative analysis of 3D-SLSM and to examine its feasibility in clinical applications, we have divided our experiments into computer-simulated sequence images and actual breast MRI cases. Subsequently, we simultaneously compared various existing 3D segmentation methods. The experimental results demonstrated that 3D-SLSM exhibited precise segmentation results for both types of experimental images. In addition, 3D-SLSM showed better results for quantitative data compared with existing 3D segmentation methods.

Lesion detection or segmentation on 3D medical image (e.g., MRI, CT-PET or hybrid modal) is a challenging task which aims to automatically mark the abnormal region on given input image. The methods to tackle the problem often construct a U-shape autoencoders to map the input to a compressed vector in latent space and predict the final probability map. However, approaches for 3D multimodal image segmentation are still not fully explored and localization accuracy can be improved by designing proper structure based on prior. In this paper, we propose a supervised contrastive approach for 3D multi-modality medical image segmentation. A hybrid 3D-2D multi-modality fusion model is designed based on the input image prior. The 3D segmentation problem is converted to a sequence prediction task by 2D decoder with contrastive modeling. The combined framework (HDC, Hybrid Dimension Contrastive) achieves comparable or even greater performance to 3D counterpart while memory efficient. We conduct extensive experiments on two multimodal datasets of brain image BraTS and our epilepsy MRI-PET. The validation results show the effectiveness of our framework.

Modeling 4D pathological changes by leveraging normative models

Mining discriminative spatial patterns in image data is an emerging subject of interest in medical imaging, meteorology, engineering, biology, and other fields. In this paper, we propose a novel approach for detecting spatial regions that are highly discriminative among different classes of three dimensional (3D) image data. The main idea of our approach is to treat the initial 3D image as a hyper-rectangle and search for discriminative regions by adaptively partitioning the space into progressively smaller hyper-rectangles (sub-regions). We use statistical information about each hyper-rectangle to guide the selectivity of the partitioning. A hyper-rectangle is partitioned only if its attribute cannot adequately discriminate among the distinct labeled classes, and it is sufficiently large for further splitting. To evaluate the discriminative power of the attributes corresponding to the detected regions, we performed classification experiments on artificial and real datasets. Our results show that the proposed method outperforms major competitors, achieving 30% and 15% better classification accuracy on synthetic and real data respectively while reducing by two orders of magnitude the number of statistical tests required by voxel-based approaches.

A common interaction with medical 2D and 3D images is to apply windows, icons, menus and pointers (WIMP) paradigm; however, WIMP might not be convenient in some situation, such as in the operating room where sterilization becomes an issue. Thus, in this project, the controller-free natural interaction on 3D medical image viewer software through a Kinect camera will be developed to replace WIMP such that surgeons can view medical images in the operating room without contacting mouse and keyboard. The interaction will include both body and figure gestures. The 3D medical image viewer software will be based on the Visualization ToolKit (VTK) library to visualize 3D image data in the DICOM (Digital Imaging and Communications in Medicine) format. Moreover, suitable controller patterns will be designed according to practical use in the operating room.

Mutual information-based 3D image Registration algorithm is shown to be an accurate, robust and more general registration method for medical image processing. However, its potential application in clinic is jeopardized by its high computational costs. Like many other 3D imaging algorithms, 3D registration needs to process vast amount of image data. The intrinsic computing characteristics of the mutual information-based registration algorithm make it even more difficult to manage and cache the input data. In this paper, we introduce our FPGA-based computing platform and detail its application in accelerating the mutual information-based 3D image registration. This platform is designed to accelerate a series of 3D medical imaging algorithms dominated by local operations. It is a System-on-Chip (SoC) architecture with an intelligent data caching system for efficient data fetching and buffering. A reconfigurable multi-pipeline execution unit in the platform is designed to perform high-speed parallel computation. This unit can be customized according to different algorithm requirements to achieve high performance. The simulation results for accelerating the mutual information based registration with our platform show that a speed-up of about 30 can be achieved when compared to a single-CPU computer. Moreover, the platform can be easily reconfigured to accelerate other 3D medical imaging algorithms.

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This article presents UCFM, a user-centered developing framework for interactive temporal data visualization. UCFM is comprised mainly by software architecture and software development methods. The software architecture describes modules in interactive temporal data visualization system and their relationships. And based on the software architecture and development practice, the software development methods summarize these specific steps to design and develop interactive temporal data visualization system. To demonstrate UCFM's validity, the development process of an interactive temporal data visualization application is illustrated.

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