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

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

Some benefits of ML 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
ML in 3d visualisation

Benefits of ML in 3D Visualisation

Machine Learning Current and Future in 3D Visualisation

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

Protein Structure Prediction

Google DeepMind Program initiated the AlphaFold project for a critical assessment of structure prediction of Proteins. Protein-structure prediction results show that using ML and deep learning algorithms the protein structures are accurately predicted in 3D visualisation. This will aid many areas of life sciences and medicines field 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 with 3D to visualise aerial and satellite images. Radars and lasers are used to create 3D visual representations in geographical areas. Now the planning on the big stage has been changed. This change includes better resource allocation of health, education, and others can be easily possible by analyzing the 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 having machine learning algorithms integrated and 3D visualization features help in designing different models for the construction industry and visualizing them on 3Ds. The virtual models on 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, (MRI) Magnetic Resonance Imaging and others. Higher imaging resolutions of 3D with the power of ML analytics allows 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 of different games. 3DAT is a machine learning (ML) in support of 3D visualisation to produce 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 using 3D printing-like technology to closely resemble real-tissue features. In 3D printing and Bioprinting, ML and 3D visualisation is helping in medical process optimization, accuracy analysis, fault identification and prediction of 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 that cannot be possible easily. Few slices of the image were used in Machine learning training which is segmented using the segment editor, software and 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 of 3D visualization was evaluated using gold particles and it is proved that the computational approach is a thousand times quicker and accurate as compared to other techniques. This testing also shows that ML algorithms can reconstruct missing information or images which 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 reliable than the current approaches available.

Challenges OF ML in 3D Visualisation

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

  • Higher storage require due to large data sets
  • Higher computational resources are required for visualising 3D images
  • Higher processing speed required 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 of Machine learning will be required with the concerned person
  • ML and 3D visualisation will end up thousands of jobs in future
  • Trust deficit such as patients are uncomfortable with machine treatment and prediction

Conclusion

The ML is giving powerful resources of analysis and handling of large data to 3D visuals with better accuracy. The 3D images having 10 to 20 features are not easy to read accurately for human beings, but ML can read it with good accuracy and help also in visualising 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 visually. However, with a lot of benefits in the future, there will be some challenges and difficulties which need to be solved. I believe ML in 3D will change the whole health and medicine field. 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