Turid Torheim1, Eirik Malinen2,3, Knut Håkon Hole4, Kjersti Vassmo Lund4, Ulf G Indahl1, Heidi Lyng5, Knut Kvaal1, Cecilia M Futsaether1. 1. a Faculty of Science and Technology , Norwegian University of Life Sciences , Ås , Norway. 2. b Department of Physics , University of Oslo , Oslo , Norway. 3. c Department of Medical Physics , Oslo University Hospital , Oslo , Norway. 4. d Department of Radiology and Nuclear Medicine , Oslo University Hospital , Oslo , Norway. 5. e Department of Radiation Biology , Oslo University Hospital , Oslo , Norway.
Abstract
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.
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.
Authors: Laura Ferreira Pinheiro Nicolielo; Jeroen Van Dessel; G Harry van Lenthe; Ivo Lambrichts; Reinhilde Jacobs Journal: Br J Radiol Date: 2018-09-17 Impact factor: 3.039
Authors: Franziska Knuth; Aurora R Groendahl; René M Winter; Turid Torheim; Anne Negård; Stein Harald Holmedal; Kine Mari Bakke; Sebastian Meltzer; Cecilia M Futsæther; Kathrine R Redalen Journal: Phys Imaging Radiat Oncol Date: 2022-05-11