ABSTRACT
Objective To investigate the time efficiency and accuracy of atlas-based auto-segmentation ( ABAS ) software with different atlas template numbers and layers of computed tomography ( CT ) scans in delineation of the target tissues of cervical cancer patients receiving radiotherapy . Methods The CT images from 20, 40, 60, 80, 100, and 120 patients with cervical cancer were separately selected as atlas templates for MIM auto-segmentation software, and the CT-based tumor volumes of another 20 patients with cervical cancer were manually contoured by physicians. The quality of contours obtained automatically from the software and manual contouring was compared by one-way analysis of variance ( ANOVA ) , randomized block ANOVA, and least significant difference t test. The impact of atlas template numbers and layers of CT scans on the accuracy and time efficiency of MIM software was analyzed based on the time spent in delineation, dice similarity coefficient, and overlap index. Results Taking manual contouring as the reference, no significant differences were observed in the accuracy and time efficiency of auto-contouring when atlas template numbers ranged from 20 to 120(all P>005). The ABAS auto-contouring significantly shortened the time for target contours when the layers of CT scans were less than 65 ( all P>005 ) , but reduced the accuracy of rectal contours (P=0000), while CT scans with 67 layers achieved the highest accuracy of rectal contours ( P= 0037 ) . Conclusions The ABAS software shows an advantage in delineation of the target tissues of cervical cancer patients receiving radiotherapy, and 20 templates are suggested to construct this atlas. The CT scans with 65 layers are recommended for patients when target tissues include the bladder, femur, and spinal cord, and CT scans with 67 layers are recommended for patients when target tissues include the rectum.
ABSTRACT
Objective To investigate the time efficiency and accuracy of atlas-based auto-segmentation ( ABAS ) software with different atlas template numbers and layers of computed tomography ( CT ) scans in delineation of the target tissues of cervical cancer patients receiving radiotherapy . Methods The CT images from 20, 40, 60, 80, 100, and 120 patients with cervical cancer were separately selected as atlas templates for MIM auto-segmentation software, and the CT-based tumor volumes of another 20 patients with cervical cancer were manually contoured by physicians. The quality of contours obtained automatically from the software and manual contouring was compared by one-way analysis of variance ( ANOVA ) , randomized block ANOVA, and least significant difference t test. The impact of atlas template numbers and layers of CT scans on the accuracy and time efficiency of MIM software was analyzed based on the time spent in delineation, dice similarity coefficient, and overlap index. Results Taking manual contouring as the reference, no significant differences were observed in the accuracy and time efficiency of auto-contouring when atlas template numbers ranged from 20 to 120(all P>005). The ABAS auto-contouring significantly shortened the time for target contours when the layers of CT scans were less than 65 ( all P>005 ) , but reduced the accuracy of rectal contours (P=0000), while CT scans with 67 layers achieved the highest accuracy of rectal contours ( P= 0037 ) . Conclusions The ABAS software shows an advantage in delineation of the target tissues of cervical cancer patients receiving radiotherapy, and 20 templates are suggested to construct this atlas. The CT scans with 65 layers are recommended for patients when target tissues include the bladder, femur, and spinal cord, and CT scans with 67 layers are recommended for patients when target tissues include the rectum.
ABSTRACT
Objective To perform a preclinical test of a delineation software based on atlas-based auto-segmentation (ABAS),to evaluate its accuracy in the delineation of organs at risk (OARs) in radiotherapy planning for nasopharyngeal carcinoma (NPC),and to provide a basis for its clinical application.Methods Using OARs manually contoured by physicians on planning-CT images of 22 patients with NPC as the standard,the automatic delineation using two different algorithms (general and head/neck) of the ABAS software were applied to the following tests:(1) to evaluate the restoration of the atlas by the software,automatic delineation was performed on copied images from each patient using the contours of OARs manually delineated on the original images as atlases;(2) to evaluate the accuracy of automatic delineation on images from various patients using a single atlas,the contours manually delineated on images from one patients were used as atlases for automatic delineation of OARs on images from other patients.Dice similarity coefficient (DSC),volume difference (Vdiff),correlation between the DSC and the volume of OARs,and efficiency difference between manual delineation and automatic delineation plus manual modification were used as indices for evaluation.Wilcoxon signed rank test and Spearman correlation analysis were used.Results The head/neck algorithm had superior restoration of the atlas over the general algorithm.The DSC was positively correlated with the volume of OARs and was higher than 0.8 for OARs larger than 1 cc in volume in the restoration test.For automatic delineation with the head/neck algorithm using a single atlas,the mean DSC and Vdiff were 0.81-0.90 and 2.73％-16.02％,respectively,for the brain stem,temporal lobes,parotids,and mandible,while the mean DSC was 0.45-0.49 for the temporomandibular joint and optic chiasm.Compared with manual delineation,automatic delineation plus manual modification saved 68％ of the time.Conclusions A preclinical test is able to determine the accuracy and conditions of the ABAS software in specific clinical application.The tested software can help to improve the efficiency of OAR delineation in radiotherapy planning for NPC.However,it is not suitable for delineation of OAR with a relatively small volume.
ABSTRACT
OBJECTIVE: Many studies have reported pre-processing effects for brain volumetry; however, no study has investigated whether non-parametric non-uniform intensity normalization (N3) correction processing results in reduced system dependency when using an atlas-based method. To address this shortcoming, the present study assessed whether N3 correction processing provides reduced system dependency in atlas-based volumetry. MATERIALS AND METHODS: Contiguous sagittal T1-weighted images of the brain were obtained from 21 healthy participants, by using five magnetic resonance protocols. After image preprocessing using the Statistical Parametric Mapping 5 software, we measured the structural volume of the segmented images with the WFU-PickAtlas software. We applied six different bias-correction levels (Regularization 10, Regularization 0.0001, Regularization 0, Regularization 10 with N3, Regularization 0.0001 with N3, and Regularization 0 with N3) to each set of images. The structural volume change ratio (%) was defined as the change ratio (%) = (100 x [measured volume - mean volume of five magnetic resonance protocols] / mean volume of five magnetic resonance protocols) for each bias-correction level. RESULTS: A low change ratio was synonymous with lower system dependency. The results showed that the images with the N3 correction had a lower change ratio compared with those without the N3 correction. CONCLUSION: The present study is the first atlas-based volumetry study to show that the precision of atlas-based volumetry improves when using N3-corrected images. Therefore, correction for signal intensity non-uniformity is strongly advised for multi-scanner or multi-site imaging trials.