Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation.

PurposeWith the advent of MR guided radiotherapy, internal organ motion can be imaged simultaneously during treatment. In this study, we evaluate the feasibility of pancreas MRI segmentation using state-of-the-art segmentation methods.Methods and materialT2 weighted HASTE and T1 weighted VIBE images were acquired on 3 patients and 2 healthy volunteers for a total of 12 imaging volumes. A novel dictionary learning (DL) method was used to segment the pancreas and compared to t mean-shift merging (MSM), distance regularized level set (DRLS), graph cuts (GC) and the segmentation results were compared to manual contours using Dice's index (DI), Hausdorff distance and shift of the-center-of-the-organ (SHIFT).ResultsAll VIBE images were successfully segmented by at least one of the auto-segmentation method with DI >0.83 and SHIFT ≤2 mm using the best automated segmentation method. The automated segmentation error of HASTE images was significantly greater. DL is statistically superior to the other methods in Dice's overlapping index. For the Hausdorff distance and SHIFT measurement, DRLS and DL performed slightly superior to the GC method, and substantially superior to MSM. DL required least human supervision and was faster to compute.ConclusionOur study demonstrated potential feasibility of automated segmentation of the pancreas on MRI images with minimal human supervision at the beginning of imaging acquisition. The achieved accuracy is promising for organ localization

Image Processing and Analysis for Preclinical and Clinical Applications

Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, “Image Processing and Analysis for Preclinical and Clinical Applications”, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis

IMPROVING RADIOTHERAPY WORKFLOW: EVALUATION AND IMPLEMENTATION OF DEEP LEARNING AUTO-SEGMENTATION IN A MULTI-USER ENVIRONMENT, AND DEVELOPMENT OF AUTOMATIC CONTOUR QUALITY ASSURANCE SYSTEM

Radiotherapy is a frequently used therapeutic modality for cancer patients. Accurately contouring of tumors and organs at risk (OARs) is critical for developing optimal treatment plans in radiotherapy, especially after the implementation of Intensity-modulated radiation therapy (IMRT) and Stereotactic Body Radiation Therapy (SBRT). The manual contouring process is time-consuming and suffers from inter-observer variations. However, manual contouring is often hindered by laborious clinical duties, leading to reduced effectiveness, and increased segmentation errors due to fatigue. Additionally, online adaptive radiation therapy(ART), which has been shown to benefit patient outcomes, places higher demands on contouring and quality assurance (QA) speed. Recently, deep learning auto-segmentation (DLAS) has emerged as an accurate tool for contouring in many anatomical sites. However, DLAS\u27s black-box nature has limited its widespread clinical implementation. Robust evaluations are required prior to the clinical implementation. In this thesis, we present our comprehensive validation approach for assessing the clinical acceptability of DLAS contours in the male pelvis region for automated prostate treatment planning. We then evaluated the DLAS model\u27s capacity for continuous improvement and generalizability and successfully adopted it in a multi-user environment. Additionally, we provided an implementation workflow for this software that can be used by other clinical users. Manual reviewing contour is a time-consuming process that is prone to errors and omissions, leading to dosimetric uncertainties and lower quality of radiation treatment. To assist with the manual contour review process, an automated contouring QA tool is necessary. We proposed a machine learning-based methodology for an automated contour quality assurance system that detects errors in manual contouring, using the precise DLAS contour as a reference. Moreover, we established a knowledge-based contour QA system that can localize and categorize contour errors for improved accuracy and efficiency. Overall, this dissertation provides a more comprehensive understanding of DLAS in a clinical multi-user environment, which will improve the quality and safety of the radiotherapy workflow

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