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Machine Learning for 3D Visualisation: What Will Be the Future?

Table of Contents

As the need for processing and storage of data increases over time, it is not easy to visualise enormous data files. Machine learning can solve the problem of visualising large data sets.

ML algorithm is robust in processing larger images, audio, and video files. So, machine learning model management in 3D frees us from the complexities of larger data sets and their manual shifting.

Some benefits of Machine Learning in 3D:

  • Intuitive user interface
  • Data visualisation
  • Discovery process
  • Realization of intelligence augmentation
  • Automating image segmentation
  • Improving 3D imaging and video
  • Computer vision
  • Better Scanning techniques
machine learning 3D visualisation

Benefits of Machine Learning in 3D Visualisation

Machine Learning Current and Future in 3D Visualisation

Machine learning model management and techniques are extensively used in 3D visualisation, but this research field is naive. Some recent uses of 3D Machine learning visualisations in different fields are discussed below.

Protein Structure Prediction

Google DeepMind Program initiated the AlphaFold project to assess the structure prediction of proteins. Protein-structure prediction results show that the protein structures are accurately predicted using ML and deep learning algorithms in 3D visualisation. This will aid many life sciences and medicine areas in diseases, cell representation, and transformation.

Visualization of Geographical Areas

Machine learning algorithms are fast on 3D data visualisation and analysis, especially for geographical areas. Now, powerful ML algorithms are used in 3D to visualise aerial and satellite images. Radars and lasers are used to create 3D visual representations in geographical areas. Now, the planning for the big stage has changed. This change includes better resource allocation for health, education, and other areas, which can be easily achieved by analyzing geography. 

3D Designing and Modelling in the Construction Industry

ML algorithms in 3D visualisation are changing the construction and civil design industry very fast. Now, powerful AutoCAD tools with machine learning algorithms integrated and 3D visualization features help in designing different models for the construction industry and visualizing them in 3D. The virtual models of geographic locations using 3D imaging look real and are accurate.

3D Visualisation of Medical Imaging

Machine learning models are aiding in the visualisation of 3D images of Computed Tomography, X-rays, micro and macro computed tomography, magnetic resonance imaging (MRI), and others. Higher 3D imaging resolutions with the power of ML analytics allow doctors to know about the details of organs without surgery. Many tests in the future will become obsolete, and images can detect the stages of diseases of any deficiency patients face.

Urban Planning and Designing

The city and regional planning fields are changing so fast that automatic transformation and detection, 3D modelling, images extraction with features and visualisation of buildings are carried out on laptops by using ML algorithms and 3D visualisation. Urban and rural planning, designing, change detection, geographical visualization, mapping, information update, monitoring, house valuation and navigation can easily be done using 3D visualisation with ML support. 

Fluorescently Labelled Cells

The ML with 3D visualisation has changed the whole dimensions of molecule-scale processes due to better visualisation and predictions than a human being. The ML models and 3D visualisation in this field is new and will take time to improve efficiency in cell scale visualisation and molecular scaling.

3D Athlete Pose Tracking

Amazon SageMaker estimates 3D posture used for 3D Athlete Tracking in preparation for different games. 3DAT is machine learning (ML) in support of 3D visualisation that produces real-time images and videos for athletes to show where they are lacking in movements.

3D Bioprinting

3D bioprinting is a method of designing biomedical equipment using cells and 3D printing-like technology to resemble real-tissue features closely. In 3D printing and Bioprinting, ML and 3D visualisation is helping in medical process optimization, accuracy analysis, fault identification and material property prediction.

Texture Classification 

Texture classification involves learning texture and patterns from user-defined markers to categorize each pixel based on resemblance to learning accurate patterns in an image. The colour auto-classification uses machine learning with 3D visualisation to automatically separate different colour pictures into labels. 

Image Segmentation of Mitochondria Blobs

ML, in aid with 3D visualisation, is helping to automate the extraction of mitochondria from the FIB-SEM stack, which cannot be easily done. A few slices of the image were used in Machine learning training, which is segmented using the segment editor software. The rest of the segmentation is done automatically, saving a lot of time and resources.

3D Visualisation of X-ray Data

Machine learning algorithms working with 3D images have a powerful ability to visualize 3D objects. This ability was evaluated using gold particles, and it proved that the computational approach is a thousand times quicker and more accurate than other techniques. This testing also shows that ML algorithms can reconstruct missing information or images that cannot be detected by the sensor or detector.

3D Visualisation of Advanced Photon

A group of scientists at Argonne National Laboratory created a novel approach for translating X-ray data into 3D visualisation pictures using ML and 3D. The creation of 3D images and visualisation is a hundred times faster and more reliable than the current approaches.

Challenges OF ML in 3D Visualisation

ML benefited from 3D visualisation, but some challenges need to be addressed.

  • Higher storage is required due to large data sets
  • Higher computational resources are needed for visualising 3D images
  • Higher processing speed necessary for processing training and deployment of ML models
  • Accurate training metrics do not necessarily produce accurate results after deployment
  • Dimensions and feature selection of 3D images 
  • Expensive Computers or machines will be required to process
  • Expert in Machine learning will be required with the concerned person
  • ML and 3D visualisation will end up thousands of jobs in future
  • Trust deficits, such as patients being uncomfortable with machine treatment and prediction

Conclusion

The ML gives powerful resources for analysing and handling extensive data to 3D visuals with better accuracy. The 3D images with 10 to 20 features are not easy for human beings to read accurately, but ML can read them with good accuracy and help visualise every feature alone. It can be analyzed which features contribute most and which contribute negligibly easily by 3D image analysis using ML modelling and analysis. This power of visualisation of complex images and analysis of 3D images using ML will change many fields in the future, and human beings can solve many problems by integrating them visually. However, with many future benefits, there will be some challenges and difficulties that need to be solved. I believe that ML in 3D will change the whole field of health and medicine. What do you think will be affected more in the future by 3D visualisation supported by ML and Deep learning?

Resources:

1-‘It will change everything’: DeepMind’s AI makes a gigantic leap in solving protein structures

2-The Potential of AI in 3D Visualization

3-3D Interactive Visualization: The New Trend In The Medical Imaging World

4-Estimating 3D pose for athlete tracking using 2D videos and Amazon SageMaker Studio

5-A Perspective on Using Machine Learning in 3D Bioprinting

6-Creating 3D Visualizations from X-ray Data with Deep Learning

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