Purpose Organ autosegmentation efforts to date have largely been focused on adult populations, due to limited availability of pediatric training data. Pediatric patients may present additional challenges for organ segmentation. This paper describes a dataset of 359 pediatric chest‐abdomen‐pelvis and abdomen‐pelvis Computed Tomography (CT) images with expert contours of up to 29 anatomical organ structures to aid in the evaluation and development of autosegmentation algorithms for pediatric CT imaging. Acquisition and validation methods The dataset collection consists of axial CT images in Digital Imaging and Communications in Medicine (DICOM) format of 180 male and 179 female pediatric chest‐abdomen‐pelvis or abdomen‐pelvis exams acquired from one of three CT scanners at Children's Wisconsin. The datasets represent random pediatric cases based upon routine clinical indications. Subjects ranged in age from 5 days to 16 years, with a mean age of 7 years. The CT acquisition, contrast, and reconstruction protocols varied across the scanner models and patients, with specifications available in the DICOM headers. Expert contours were manually labeled for up to 29 organ structures per subject. Not all contours are available for all subjects, due to limited field of view or unreliable contouring due to high noise. Data format and usage notes The data are available on The Cancer Imaging Archive (TCIA_ (https://www.cancerimagingarchive.net/) under the collection Pediatric‐CT‐SEG. The axial CT image slices for each subject are available in DICOM format. The expert contours are stored in a single DICOM RTSTRUCT file for each subject. The contour names are listed in Table 2. Potential applications This dataset will enable the evaluation and development of organ autosegmentation algorithms for pediatric populations, which exhibit variations in organ shape and size across age. Automated organ segmentation from CT images has numerous applications including radiation therapy, diagnostic tasks, surgical planning, and patient‐specific organ dose estimation.
This study developed and evaluated a fully convolutional network (FCN) for pediatric CT organ segmentation and investigated the generalizability of the FCN across image heterogeneities such as CT scanner model protocols and patient age. We also evaluated the autosegmentation models as part of a software tool for patient-specific CT dose estimation. Methods: A collection of 359 pediatric CT datasets with expert organ contours were used for model development and evaluation. Autosegmentation models were trained for each organ using a modified FCN 3D V-Net. An independent test set of 60 patients was withheld for testing. To evaluate the impact of CT scanner model protocol and patient age heterogeneities, separate models were trained using a subset of scanner model protocols and pediatric age groups. Train and test sets were split to answer questions about the generalizability of pediatric FCN autosegmentation models to unseen age groups and scanner model protocols, as well as the merit of scanner model protocol or age-groupspecific models. Finally, the organ contours resulting from the autosegmentation models were applied to patient-specific dose maps to evaluate the impact of segmentation errors on organ dose estimation. Results: Results demonstrate that the autosegmentation models generalize to CT scanner acquisition and reconstruction methods which were not present in the training dataset. While models are not equally generalizable across age groups, age-group-specific models do not hold any advantage over combining heterogeneous age groups into a single training set. Dice similarity coefficient (DSC) and mean surface distance results are presented for 19 organ structures, for example, median DSC of 0.52 (duodenum), 0.74 (pancreas), 0.92 (stomach), and 0.96 (heart). The FCN models achieve a mean dose error within 5% of expert segmentations for all 19 organs except for the spinal canal, where the mean error was 6.31%. Conclusions: Overall, these results are promising for the adoption of FCN autosegmentation models for pediatric CT, including applications for patientspecific CT dose estimation.
Purpose: In radiotherapy treatments of breast patients, respirations may introduce uncertainties in target and heart locations. This study is to investigate the dosimetric impacts of these uncertainties in breast radiation treatments. Method and Materials: A 4D CT scan and a conventional helical CT scan set were acquired on each of 7 left breast patients and 5 right breast patients. Using the helical CT scan, a conventional 3D conformal plan, consisting of two tangential beams, was generated per physician's evaluation and decision. The 4D CT scan set was divided into 10 phases over the respiratory cycle. On each phase, treatment target and heart were contoured. Dose distributions were generated using the same beams as in the conventional plan. Software was developed to compute the cumulative dose distribution (4D doses) from all the phases. This 4D CT image based cumulative dose distribution would be closer to that in reality with motions taken into account. Various dosimetric parameters were obtained for treatment target and heart from the conventional plan and from the 4D cumulative dose distributions and compared to deduce the motion induced dosimetric impacts in breast radiation treatments. Studies were performed for both whole and partial breast treatments. Results: For whole breast treatment, the motion induced changes in D95, Dmax, and Dmin of PTV were 0.88% ± 20%, −0.28 ± 0.65%, and −10.17% ± 47%, respectively. For left breast, the motion induced Dmax change in heart was 22% ± 48%. For partial breast treatments, the motion induced changes in V90 and Dmin of CTV were 1.6% ± 2.7% and 3% ± 4%, respectively. Conclusions: Breathing motion may cause cold spots in the whole breast treatment, and may compromise treatment quality for some patients. It may also increase heart maximum dose. However, for the partial breast treatment, the motion impact may be insignificant with properly selected margin size. Supported in part by Varian Medical Systems.
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