Home PFEE GE Healthcare: Deep Learning Inpainting
Post
Cancel

PFEE GE Healthcare: Deep Learning Inpainting

Lien de la note Hackmd

How to get a volume ?

  • Turn around the patient
  • X ray acquisition in different angles

Artifacts

Different types of artefact:

  • Motion artifact
  • Metal artifact
  • Ring artifacts (detector)

State of the art

  • Where
    • Correction applied on volumes

Inpainting

  • Fill selected image area
  • Requires having the mask of the missing parts

Methods

Interpolation 2D

Interpolation algorithm from skimage to create a basline:

  • Nearest neighbor
  • Linear

U-NET 2D

Improvements ?

  • Conv2D/3D do not consider the mask
  • Losses (MSE/MAE) do not consider the mask

Partial convolution

  • Presented by Nvidia in 2018
  • Mask area is much less visible and overall results are improves

Keras ?

Loss improvement

  • Using a train VGG
    • deep learning classifier
    • Layers used: 3rd, 6th and 10th

Data

Experiments

Goal

  • 2D
    • Perfomance machine learning
    • Added value
  • 3D
    • Adding temporal gives best results
    • Can we be more memory efficient using patches ?

Evaluation method

  • Quantitative evaluation
  • MSE
  • MAE
  • SSIM
    • Structural similarity
  • PSNR
    • Peak To Signal Noise Ratio
    • More quantitative than qualitative
  • Quality eval
    • Eval by human eye

Results

Qualitative 2D

Ribs reconstruction

Qualitative 2D+T

Analysis

  • Machine learning can be used for this task
  • PConv and VGG loss are the best improvements

Conclusion

  • Implementation of PConv2D and PConv3D
  • Promising resultls
  • Kickstarted GE exploration and gave them insights on their future work
  • Had fun with advance machine learning

Questions

Guillaume Tochon

Le papier a ete utilise sur des images de scan ?

Non sur des images naturelles

Expliquer ‘smoothing loss’

Dilatation verticale et horizontale des resultats

Elodie Puybareau

Generation des artefacts: probleme avec modele 2D+T, pourquoi blanc alors que modele 2D noir ?

Les modeles 2D sont aussi blanc sur les images

Eleves

Exemple d’applications concretes ?

Application de corrections permet d’avoir des images pouvant etre travaillees pour un medecin

Genration des artefacts aleatoires ?

Oui pour la position et rotation en 3D mais sinon non, pas de perte de temps a generer de la donnee

Tester avec des formes differentes ?

Oui avec des coins et des aiguilles

Generer un nombre infini de donnees, probleme de fit ?

Oui

Interpolation lineaire plus simple ?

Oui mais beaucoup de stries et n’arrive pas a reconstruire certaines parties

Retour GE Healthcare

Bonne organisation, bon avancement des projets mais baisse d’activite lors d’examens, groupe autonome

This post is licensed under CC BY 4.0 by the author.