Autodelineation of cervical cancers using multiparametric magnetic resonance imaging and machine learning

Acta Oncol. 2017 Jun;56(6):806-812. doi: 10.1080/0284186X.2017.1285499. Epub 2017 Feb 8.


Background: Tumour delineation is a challenging, time-consuming and complex part of radiotherapy planning. In this study, an automatic method for delineating locally advanced cervical cancers was developed using a machine learning approach.

Materials and methods: A method for tumour segmentation based on image voxel classification using Fisher?s Linear Discriminant Analysis (LDA) was developed. This was applied to magnetic resonance (MR) images of 78 patients with locally advanced cervical cancer. The segmentation was based on multiparametric MRI consisting of T2- weighted (T2w), T1-weighted (T1w) and dynamic contrast-enhanced (DCE) sequences, and included intensity and spatial information from the images. The model was trained and assessed using delineations made by two radiologists.

Results: Segmentation based on T2w or T1w images resulted in mean sensitivity and specificity of 94% and 52%, respectively. Including DCE-MR images improved the segmentation model?s performance significantly, giving mean sensitivity and specificity of 85?93%. Comparisons with radiologists? tumour delineations gave Dice similarity coefficients of up to 0.44.

Conclusion: Voxel classification using a machine learning approach is a flexible and fully automatic method for tumour delineation. Combining all relevant MR image series resulted in high sensitivity and specificity. Moreover, the presented method can be extended to include additional imaging modalities.

MeSH terms

  • Algorithms
  • Contrast Media / metabolism
  • Female
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Machine Learning*
  • Magnetic Resonance Imaging / methods*
  • Organs at Risk / radiation effects
  • Radiotherapy Dosage
  • Radiotherapy Planning, Computer-Assisted / methods*
  • Uterine Cervical Neoplasms / diagnostic imaging
  • Uterine Cervical Neoplasms / metabolism
  • Uterine Cervical Neoplasms / pathology*


  • Contrast Media