METHODOLOGY TO ASSESS AUTOMATED ATLAS-BASED SEGMENTATIONS AND INTER- AND INTRA-OBSERVER VARIABILITY IN THE DELINEATION OF THE PROSTATE BED

The purpose of the study was to assess the accuracy, validity and time-savings of automated atlas-based segmentations (AABS). Further, the study assessed the inter- and intra­ observer variability in the delineation of the prostate bed (PB) and the five regions of interest for postoperative conformal radiotherapy in prostate cancer patients. Finally, the study reports on the development of an appropriate methodology for similar studies. Seventy-five DICOM Computed Tomography (CT) datasets were obtained to create the prostate bed atlas and another five datasets were retrospectively contoured by the atlas builder, the expert panel and the AABS tool. Consensus segmentations (CS) were also generated. The mean dice similarity coefficient comparing the edited AABS and CS was 0.67, 0.88, 0.93, 0.92, 0.54 and 0.78 for the PB, bladder, left- and right femoral head, penile bulb and rectum, respectively. Significant inter­ observer variation was observed in the PB and bilateral femoral heads. Significant time savings were obtained using the average AABS editing time (p = 0.003) versus the manual contouring time. We successfully developed a methodology and validated the AABS tool for routine clinical use

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes. In this work we propose an approach to 3D image segmentation based on a volumetric, fully convolutional, neural network. Our CNN is trained end-to-end on MRI volumes depicting prostate, and learns to predict segmentation for the whole volume at once. We introduce a novel objective function, that we optimise during training, based on Dice coefficient. In this way we can deal with situations where there is a strong imbalance between the number of foreground and background voxels. To cope with the limited number of annotated volumes available for training, we augment the data applying random non-linear transformations and histogram matching. We show in our experimental evaluation that our approach achieves good performances on challenging test data while requiring only a fraction of the processing time needed by other previous methods

Validation Strategies Supporting Clinical Integration of Prostate Segmentation Algorithms for Magnetic Resonance Imaging

Segmentation of the prostate in medical images is useful for prostate cancer diagnosis and therapy guidance. However, manual segmentation of the prostate is laborious and time-consuming, with inter-observer variability. The focus of this thesis was on accuracy, reproducibility and procedure time measurement for prostate segmentation on T2-weighted endorectal magnetic resonance imaging, and assessment of the potential of a computer-assisted segmentation technique to be translated to clinical practice for prostate cancer management. We collected an image data set from prostate cancer patients with manually-delineated prostate borders by one observer on all the images and by two other observers on a subset of images. We used a complementary set of error metrics to measure the different types of observed segmentation errors. We compared expert manual segmentation as well as semi-automatic and automatic segmentation approaches before and after manual editing by expert physicians. We recorded the time needed for user interaction to initialize the semi-automatic algorithm, algorithm execution, and manual editing as necessary. Comparing to manual segmentation, the measured errors for the algorithms compared favourably with observed differences between manual segmentations. The measured average editing times for the computer-assisted segmentation were lower than fully manual segmentation time, and the algorithms reduced the inter-observer variability as compared to manual segmentation. The accuracy of the computer-assisted approaches was near to or within the range of observed variability in manual segmentation. The recorded procedure time for prostate segmentation was reduced using computer-assisted segmentation followed by manual editing, compared to the time required for fully manual segmentation