Multi-institutional quantitative evaluation and clinical validation of Smart Probabilistic Image Contouring Engine (SPICE) autosegmentation of target structures and normal tissues on computer tomography images in the head and neck, thorax, liver, and male pelvis areas.

PURPOSE Clinical validation and quantitative evaluation of computed tomography (CT) image autosegmentation using Smart Probabilistic Image Contouring Engine (SPICE). METHODS AND MATERIALS CT images of 125 treated patients (32 head and neck [HN], 40 thorax, 23 liver, and 30 prostate) in 7 independent institutions were autosegmented using SPICE and computational times were recorded. The number of structures autocontoured were 25 for the HN, 7 for the thorax, 3 for the liver, and 6 for the male pelvis regions. Using the clinical contours as reference, autocontours of 22 selected structures were quantitatively evaluated using Dice Similarity Coefficient (DSC) and Mean Slice-wise Hausdorff Distance (MSHD). All 40 autocontours were evaluated by a radiation oncologist from the institution that treated the patients. RESULTS The mean computational times to autosegment all the structures using SPICE were 3.1 to 11.1 minutes per patient. For the HN region, the mean DSC was >0.70 for all evaluated structures, and the MSHD ranged from 3.2 to 10.0 mm. For the thorax region, the mean DSC was 0.95 for the lungs and 0.90 for the heart, and the MSHD ranged from 2.8 to 12.8 mm. For the liver region, the mean DSC was >0.92 for all structures, and the MSHD ranged from 5.2 to 15.9 mm. For the male pelvis region, the mean DSC was >0.76 for all structures, and the MSHD ranged from 4.8 to 10.5 mm. Out of the 40 autocontoured structures reviews by experts, 25 were scored useful as autocontoured or with minor edits for at least 90% of the patients and 33 were scored useful autocontoured or with minor edits for at least 80% of the patients. CONCLUSIONS Compared with manual contouring, autosegmentation using SPICE for the HN, thorax, liver, and male pelvis regions is efficient and shows significant promise for clinical utility.

[1]  Josien P. W. Pluim,et al.  Fast Automatic Multi-atlas Segmentation of the Prostate from 3D MR Images , 2011, Prostate Cancer Imaging.

[2]  Issam El Naqa,et al.  Pelvic normal tissue contouring guidelines for radiation therapy: a Radiation Therapy Oncology Group consensus panel atlas. , 2012, International journal of radiation oncology, biology, physics.

[3]  Wei Li,et al.  Learning Image Context for Segmentation of Prostate in CT-Guided Radiotherapy , 2011, MICCAI.

[4]  Joshua D. Lawson,et al.  Evaluation of automatic atlas-based lymph node segmentation for head-and-neck cancer. , 2010, International journal of radiation oncology, biology, physics.

[5]  Vladimir Pekar,et al.  Auto-segmentation of normal and target structures in head and neck CT images: a feature-driven model-based approach. , 2011, Medical physics.

[6]  M. Stock,et al.  Critical discussion of evaluation parameters for inter-observer variability in target definition for radiation therapy , 2012, Strahlentherapie und Onkologie.

[7]  Martin Styner,et al.  Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets , 2009, IEEE Transactions on Medical Imaging.

[8]  A. Lomax,et al.  Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer , 2012, Radiation oncology.

[9]  V. Pekar,et al.  Head and Neck Auto-segmentation Challenge , 2009, The MIDAS Journal.

[10]  P. Kunz,et al.  A qualitative and a quantitative analysis of an auto-segmentation module for prostate cancer. , 2009, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[11]  Xiao Han,et al.  Clinical validation of atlas-based auto-segmentation of multiple target volumes and normal tissue (swallowing/mastication) structures in the head and neck. , 2011, International journal of radiation oncology, biology, physics.

[12]  Hervé Delingette,et al.  Automatic Segmentation of Bladder and Prostate Using Coupled 3D Deformable Models , 2007, MICCAI.

[13]  Wolfgang A. Tome,et al.  Emphasizing conformal avoidance versus target definition for IMRT planning in head-and-neck cancer. , 2010, International journal of radiation oncology, biology, physics.

[14]  Alicia Y Toledano,et al.  An evaluation of the variability of tumor-shape definition derived by experienced observers from CT images of supraglottic carcinomas (ACRIN protocol 6658). , 2007, International journal of radiation oncology, biology, physics.

[15]  Michael G Jameson,et al.  A review of methods of analysis in contouring studies for radiation oncology , 2010, Journal of medical imaging and radiation oncology.

[16]  B. Dawant,et al.  Liver segmentation in living liver transplant donors: comparison of semiautomatic and manual methods. , 2005, Radiology.

[17]  Michael Lock,et al.  Technology assessment of automated atlas based segmentation in prostate bed contouring , 2011, Radiation oncology.

[18]  Marcel van Herk,et al.  Observer variation in target volume delineation of lung cancer related to radiation oncologist-computer interaction: a 'Big Brother' evaluation. , 2005, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[19]  Johannes A Langendijk,et al.  Delineation guidelines for organs at risk involved in radiation-induced salivary dysfunction and xerostomia. , 2009, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[20]  E. R. van den Heuvel,et al.  3D Variation in delineation of head and neck organs at risk , 2012, Radiation oncology.

[21]  S. Davies,et al.  Big brother : Britain's web of surveillance and the new technological order , 1996 .