The evaluation of a deformable image registration segmentation technique for semi-automating internal target volume (ITV) production from 4DCT images of lung stereotactic body radiotherapy (SBRT) patients.

PURPOSE To evaluate a deformable image registration (DIR) segmentation technique for semi-automating ITV production from 4DCT for lung patients, in terms of accuracy and efficiency. METHODS Twenty-five stereotactic body radiotherapy lung patients were selected in this retrospective study. ITVs were manually delineated by an oncologist and semi-automatically produced by propagating the GTV manually delineated on the mid-ventilation phase to all other phases using two different DIR algorithms, using commercial software. The two ITVs produced by DIR were compared to the manually delineated ITV using the dice similarity coefficient (DSC), mean distance between agreement and normalised DSC. DIR-produced ITVs were assessed for their clinical suitability and also the time savings were estimated. RESULTS Eighteen out of 25 ITVs had normalised DSC>1 indicating an agreement with the manually produced ITV within 1mm uncertainty. Four of the other seven ITVs were deemed clinically acceptable and three would require a small amount of editing. In general, ITVs produced by DIR were smoother than those produced by manual delineation. It was estimated that using this technique would save clinicians on average 28 min/patient. CONCLUSIONS ABAS was found to be a useful tool in the production of ITVs for lung patients. The ITVs produced are either immediately clinically acceptable or require minimal editing. This approach represents a significant time saving for clinicians.

[1]  Bernard Dubray,et al.  Conformal radiotherapy for lung cancer: different delineation of the gross tumor volume (GTV) by radiologists and radiation oncologists. , 2002, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[2]  Quan Chen,et al.  Automatic re-contouring in 4D radiotherapy , 2006, Physics in medicine and biology.

[3]  George T. Y. Chen,et al.  Four-dimensional image-based treatment planning: Target volume segmentation and dose calculation in the presence of respiratory motion. , 2005, International journal of radiation oncology, biology, physics.

[4]  George Starkschall,et al.  Comparison of rigid and adaptive methods of propagating gross tumor volume through respiratory phases of four-dimensional computed tomography image data set. , 2008, International journal of radiation oncology, biology, physics.

[5]  D. Hill,et al.  Non-rigid image registration: theory and practice. , 2004, The British journal of radiology.

[6]  B Norrlinger,et al.  A novel four-dimensional radiotherapy method for lung cancer: imaging, treatment planning and delivery , 2006, Physics in medicine and biology.

[7]  R. Mohan,et al.  Validation of a model-based segmentation approach to propagating normal anatomic regions of interest through the 10 phases of respiration. , 2008, International journal of radiation oncology, biology, physics.

[8]  Jan-Jakob Sonke,et al.  Comparison of different strategies to use four-dimensional computed tomography in treatment planning for lung cancer patients. , 2008, International journal of radiation oncology, biology, physics.

[9]  George Starkschall,et al.  Determination of patient-specific internal gross tumor volumes for lung cancer using four-dimensional computed tomography , 2009, Radiation oncology.

[10]  Suresh Senan,et al.  Use of maximum intensity projections (MIP) for target volume generation in 4DCT scans for lung cancer. , 2005, International journal of radiation oncology, biology, physics.

[11]  Carri K Glide-Hurst,et al.  A simplified method of four-dimensional dose accumulation using the mean patient density representation. , 2008, Medical physics.

[12]  Jean-Philippe Thirion,et al.  Image matching as a diffusion process: an analogy with Maxwell's demons , 1998, Medical Image Anal..

[13]  Michael Bremer,et al.  The delineation of target volumes for radiotherapy of lung cancer patients. , 2009, Radiotherapy and Oncology.

[14]  R. Mohan,et al.  Acquiring a four-dimensional computed tomography dataset using an external respiratory signal. , 2003, Physics in medicine and biology.

[15]  Xiao Han,et al.  Atlas-Based Auto-segmentation of Head and Neck CT Images , 2008, MICCAI.

[16]  J. Roeske,et al.  Use of Autosegmentation Software to Contour Normal Tissues in Multi-fractional HDR Brachytherapy for Cervical Cancer , 2009 .

[17]  Arjan Bel,et al.  Definition of gross tumor volume in lung cancer: inter-observer variability. , 2002, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[18]  L. R. Dice Measures of the Amount of Ecologic Association Between Species , 1945 .

[19]  Stewart Gaede,et al.  Inter-observer and intra-observer reliability for lung cancer target volume delineation in the 4D-CT era. , 2010, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[20]  Suresh Senan,et al.  Improving target delineation on 4-dimensional CT scans in stage I NSCLC using a deformable registration tool. , 2010, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[21]  Mathieu De Craene,et al.  Tumour delineation and cumulative dose computation in radiotherapy based on deformable registration of respiratory correlated CT images of lung cancer patients. , 2007, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[22]  Lei Dong,et al.  Reduce in variation and improve efficiency of target volume delineation by a computer-assisted system using a deformable image registration approach. , 2007, International journal of radiation oncology, biology, physics.

[23]  Alfons G H Kessels,et al.  Comparison of the effectiveness of radiotherapy with photons, protons and carbon-ions for non-small cell lung cancer: a meta-analysis. , 2010, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[24]  Carsten Brink,et al.  Deviations in delineated GTV caused by artefacts in 4DCT. , 2010, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[25]  R. Muirhead,et al.  Use of Maximum Intensity Projections (MIPs) for Target Outlining in 4DCT Radiotherapy Planning , 2008, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[26]  Lech Papiez,et al.  Do maximum intensity projection images truly capture tumor motion? , 2009, International journal of radiation oncology, biology, physics.

[27]  Timothy Solberg,et al.  A study on the dosimetric accuracy of treatment planning for stereotactic body radiation therapy of lung cancer using average and maximum intensity projection images. , 2010, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.