Segmenting CT prostate images using population and patient-specific statistics for radiotherapy: Segmenting CT prostate images for radiotherapy

Purpose: In the segmentation of sequential treatment-time CT prostate images acquired in image-guided radiotherapy, accurately capturing the intrapatient variation of the patient under therapy is more important than capturing interpatient variation. However, using the traditional deformable-model-based segmentation methods, it is difficult to capture intrapatient variation when the number of samples from the same patient is limited. This article presents a new deformable model, designed specifically for segmenting sequential CT images of the prostate, which leverages both population and patient-specific statistics to accurately capture the intrapatient variation of the patient under therapy

Accurate Segmentation of CT Male Pelvic Organs via Regression-Based Deformable Models and Multi-Task Random Forests

Segmenting male pelvic organs from CT images is a prerequisite for prostate cancer radiotherapy. The efficacy of radiation treatment highly depends on segmentation accuracy. However, accurate segmentation of male pelvic organs is challenging due to low tissue contrast of CT images, as well as large variations of shape and appearance of the pelvic organs. Among existing segmentation methods, deformable models are the most popular, as shape prior can be easily incorporated to regularize the segmentation. Nonetheless, the sensitivity to initialization often limits their performance, especially for segmenting organs with large shape variations. In this paper, we propose a novel approach to guide deformable models, thus making them robust against arbitrary initializations. Specifically, we learn a displacement regressor, which predicts 3D displacement from any image voxel to the target organ boundary based on the local patch appearance. This regressor provides a nonlocal external force for each vertex of deformable model, thus overcoming the initialization problem suffered by the traditional deformable models. To learn a reliable displacement regressor, two strategies are particularly proposed. 1) A multi-task random forest is proposed to learn the displacement regressor jointly with the organ classifier; 2) an auto-context model is used to iteratively enforce structural information during voxel-wise prediction. Extensive experiments on 313 planning CT scans of 313 patients show that our method achieves better results than alternative classification or regression based methods, and also several other existing methods in CT pelvic organ segmentation

A learning-based CT prostate segmentation method via joint transductive feature selection and regression

In1 recent years, there has been a great interest in prostate segmentation, which is a important and challenging task for CT image guided radiotherapy. In this paper, a learning-based segmentation method via joint transductive feature selection and transductive regression is presented, which incorporates the physician’s simple manual specification (only taking a few seconds), to aid accurate segmentation, especially for the case with large irregular prostate motion. More specifically, for the current treatment image, experienced physician is first allowed to manually assign the labels for a small subset of prostate and non-prostate voxels, especially in the first and last slices of the prostate regions. Then, the proposed method follows the two step: in prostate-likelihood estimation step, two novel algorithms: tLasso and wLapRLS, will be sequentially employed for transductive feature selection and transductive regression, respectively, aiming to generate the prostate-likelihood map. In multi-atlases based label fusion step, the final segmentation result will be obtained according to the corresponding prostate-likelihood map and the previous images of the same patient. The proposed method has been substantially evaluated on a real prostate CT dataset including 24 patients with 330 CT images, and compared with several state-of-the-art methods. Experimental results show that the proposed method outperforms the state-of-the-arts in terms of higher Dice ratio, higher true positive fraction, and lower centroid distances. Also, the results demonstrate that simple manual specification can help improve the segmentation performance, which is clinically feasible in real practice

Learning image context for segmentation of the prostate in CT-guided radiotherapy

Accurate segmentation of prostate is the key to the success of external beam radiotherapy of prostate cancer. However, accurate segmentation of prostate in computer tomography (CT) images remains challenging mainly due to three factors: (1) low image contrast between the prostate and its surrounding tissues, (2) unpredictable prostate motion across different treatment days, and (3) large variations of intensities and shapes of bladder and rectum around the prostate. In this paper, an online-learning and patient-specific classification method based on the location-adaptive image context is presented to deal with all these challenging issues and achieve the precise segmentation of prostate in CT images. Specifically, two sets of location-adaptive classifiers are placed, respectively, along the two coordinate directions of the planning image space of a patient, and further trained with the planning image and also the previous-segmented treatment images of the same patient to jointly perform prostate segmentation for a new treatment image (of the same patient). In particular, each location-adaptive classifier, which itself consists of a set of sequential sub-classifiers, is recursively trained with both the static image appearance features and the iteratively-updated image context features (extracted at different scales and orientations) for better identification of each prostate region. The proposed learning-based prostate segmentation method has been extensively evaluated on 161 images of 11 patients, each with more than 9 daily treatment 3D CT images. Our method achieves the mean Dice value 0.908 and the mean ± SD of average surface distance (ASD) value 1.40 ± 0.57 mm. Its performance is also compared with three prostate segmentation methods, indicating the best segmentation accuracy by the proposed method among all methods under comparison

PSMA PET as a predictive tool for sub-regional importance estimates in the parotid gland

Objective: Xerostomia (subjective dry mouth) and radiation-induced salivary gland dysfunction remain a common side effect for head-and-neck radiotherapy patients, and attempts have been made to quantify the intra-parotid dose response. Here, we aim to compare several models of parotid gland regional importance with prostate specific membrane antigen (PSMA) positron emission tomography (PET), which has high concentrations of uptake in salivary glands and has been previously suggested to relate to gland functionality. Furthermore, we develop a predictive model of Clark et al.'s relative importance using radiomic features, and demonstrate a methodology for predicting patient-specific importance deviations from the population. Approach: Intra-parotid uptake was compared with four regional importance models using [18F]DCFPyL PSMA PET images. The correlation of uptake and importance was ascertained when numerous non-overlapping sub-regions were defined, while a paired t-test was used when binary regions were defined. Radiomic PSMA PET/CT features within Clark et al.'s sub-regions were used to develop a predictive model of population importance. Main Results: Clark et al.'s relative importance regions were significantly (p < 0.02) anti-correlated with PSMA PET uptake. Van Luijk et al.'s critical regions had significantly lower (p < 0.01) uptake than in non-critical regions. Kernel Ridge Regression with principal component analysis feature selection performed best over test sets (Mean Absolute Error = 0.08. Deblurring PSMA PET images with neural blind deconvolution strengthened correlations and improved model performance. Significance: This study suggests that regions of relatively low PSMA PET concentration in parotid glands may exhibit relatively high dose-sensitivity. We've demonstrated the ability of PSMA PET radiomic features for predicting relative importance within the parotid glands.Comment: 9 Figures, 7 Table

Accurate Segmentation of CT Pelvic Organs via Incremental Cascade Learning and Regression-based Deformable Models

Accurate segmentation of male pelvic organs from computed tomography (CT) images is important in image guided radiotherapy (IGRT) of prostate cancer. The efficacy of radiation treatment highly depends on the segmentation accuracy of planning and treatment CT images. Clinically manual delineation is still generally performed in most hospitals. However, it is time consuming and suffers large inter-operator variability due to the low tissue contrast of CT images. To reduce the manual efforts and improve the consistency of segmentation, it is desirable to develop an automatic method for rapid and accurate segmentation of pelvic organs from planning and treatment CT images. This dissertation marries machine learning and medical image analysis for addressing two fundamental yet challenging segmentation problems in image guided radiotherapy of prostate cancer. Planning-CT Segmentation. Deformable models are popular methods for planning-CT segmentation. However, they are well known to be sensitive to initialization and ineffective in segmenting organs with complex shapes. To address these limitations, this dissertation investigates a novel deformable model named regression-based deformable model (RDM). Instead of locally deforming the shape model, in RDM the deformation at each model point is explicitly estimated from local image appearance and used to guide deformable segmentation. As the estimated deformation can be long-distance and is spatially adaptive to each model point, RDM is insensitive to initialization and more flexible than conventional deformable models. These properties render it very suitable for CT pelvic organ segmentation, where initialization is difficult to get and organs may have complex shapes. Treatment-CT Segmentation. Most existing methods have two limitations when they are applied to treatment-CT segmentation. First, they have a limited accuracy because they overlook the availability of patient-specific data in the IGRT workflow. Second, they are time consuming and may take minutes or even longer for segmentation. To improve both accuracy and efficiency, this dissertation combines incremental learning with anatomical landmark detection for fast localization of the prostate in treatment CT images. Specifically, cascade classifiers are learned from a population to automatically detect several anatomical landmarks in the image. Based on these landmarks, the prostate is quickly localized by aligning and then fusing previous segmented prostate shapes of the same patient. To improve the performance of landmark detection, a novel learning scheme named "incremental learning with selective memory" is proposed to personalize the population-based cascade classifiers to the patient under treatment. Extensive experiments on a large dataset show that the proposed method achieves comparable accuracy to the state of the art methods while substantially reducing runtime from minutes to just 4 seconds.Doctor of Philosoph

Hardware acceleration using FPGAs for adaptive radiotherapy

Adaptive radiotherapy (ART) seeks to improve the accuracy of radiotherapy by adapting the treatment based on up-to-date images of the patient's anatomy captured at the time of treatment delivery. The amount of image data, combined with the clinical time requirements for ART, necessitates automatic image analysis to adapt the treatment plan. Currently, the computational effort of the image processing and plan adaptation means they cannot be completed in a clinically acceptable timeframe. This thesis aims to investigate the use of hardware acceleration on Field Programmable Gate Arrays (FPGAs) to accelerate algorithms for segmenting bony anatomy in Computed Tomography (CT) scans, to reduce the plan adaptation time for ART. An assessment was made of the overhead incurred by transferring image data to an FPGA-based hardware accelerator using the industry-standard DICOM protocol over an Ethernet connection. The rate was found to be likely to limit the performanceof hardware accelerators for ART, highlighting the need for an alternative method of integrating hardware accelerators with existing radiotherapy equipment. A clinically-validated segmentation algorithm was adapted for implementation in hardware. This was shown to process three-dimensional CT images up to 13.81 times faster than the original software implementation. The segmentations produced by the two implementations showed strong agreement. Modifications to the hardware implementation were proposed for segmenting fourdimensional CT scans. This was shown to process image volumes 14.96 times faster than the original software implementation, and the segmentations produced by the two implementations showed strong agreement in most cases.A second, novel, method for segmenting four-dimensional CT data was also proposed. The hardware implementation executed 1.95 times faster than the software implementation. However, the algorithm was found to be unsuitable for the global segmentation task examined here, although it may be suitable as a refining segmentation in the context of a larger ART algorithm.Adaptive radiotherapy (ART) seeks to improve the accuracy of radiotherapy by adapting the treatment based on up-to-date images of the patient's anatomy captured at the time of treatment delivery. The amount of image data, combined with the clinical time requirements for ART, necessitates automatic image analysis to adapt the treatment plan. Currently, the computational effort of the image processing and plan adaptation means they cannot be completed in a clinically acceptable timeframe. This thesis aims to investigate the use of hardware acceleration on Field Programmable Gate Arrays (FPGAs) to accelerate algorithms for segmenting bony anatomy in Computed Tomography (CT) scans, to reduce the plan adaptation time for ART. An assessment was made of the overhead incurred by transferring image data to an FPGA-based hardware accelerator using the industry-standard DICOM protocol over an Ethernet connection. The rate was found to be likely to limit the performanceof hardware accelerators for ART, highlighting the need for an alternative method of integrating hardware accelerators with existing radiotherapy equipment. A clinically-validated segmentation algorithm was adapted for implementation in hardware. This was shown to process three-dimensional CT images up to 13.81 times faster than the original software implementation. The segmentations produced by the two implementations showed strong agreement. Modifications to the hardware implementation were proposed for segmenting fourdimensional CT scans. This was shown to process image volumes 14.96 times faster than the original software implementation, and the segmentations produced by the two implementations showed strong agreement in most cases.A second, novel, method for segmenting four-dimensional CT data was also proposed. The hardware implementation executed 1.95 times faster than the software implementation. However, the algorithm was found to be unsuitable for the global segmentation task examined here, although it may be suitable as a refining segmentation in the context of a larger ART algorithm

Deformable models for adaptive radiotherapy planning

Radiotherapy is the most widely used treatment for cancer, with 4 out of 10 cancer patients receiving radiotherapy as part of their treatment. The delineation of gross tumour volume (GTV) is crucial in the treatment of radiotherapy. An automatic contouring system would be beneficial in radiotherapy planning in order to generate objective, accurate and reproducible GTV contours. Image guided radiotherapy (IGRT) acquires patient images just before treatment delivery to allow any necessary positional correction. Consequently, real-time contouring system provides an opportunity to adopt radiotherapy on the treatment day. In this thesis, freely deformable models (FDM) and shape constrained deformable models (SCDMs) were used to automatically delineate the GTV for brain cancer and prostate cancer. Level set method (LSM) is a typical FDM which was used to contour glioma on brain MRI. A series of low level image segmentation methodologies are cascaded to form a case-wise fully automatic initialisation pipeline for the level set function. Dice similarity coefficients (DSCs) were used to evaluate the contours. Results shown a good agreement between clinical contours and LSM contours, in 93% of cases the DSCs was found to be between 60% and 80%. The second significant contribution is a novel development to the active shape model (ASM), a profile feature was selected from pre-computed texture features by minimising the Mahalanobis distance (MD) to obtain the most distinct feature for each landmark, instead of conventional image intensity. A new group-wise registration scheme was applied to solve the correspondence definition within the training data. This ASM model was used to delineated prostate GTV on CT. DSCs for this case was found between 0.75 and 0.91 with the mean DSC 0.81. The last contribution is a fully automatic active appearance model (AAM) which captures image appearance near the GTV boundary. The image appearance of inner GTV was discarded to spare the potential disruption caused by brachytherapy seeds or gold markers. This model outperforms conventional AAM at the prostate base and apex region by involving surround organs. The overall mean DSC for this case is 0.85