Boundary Coding Representation for Organ Segmentation in Prostate Cancer Radiotherapy.
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ABSTRACT:
SUBMITTER: Wang S
PROVIDER: S-EPMC8202780 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The segmentation of prostate in CT images is of essential importance to external beam radiotherapy, which is one of the major treatments for prostate cancer nowadays. During the radiotherapy, the prostate is radiated by high-energy x rays from different directions. In order to maximize the dose to the cancer and minimize the dose to the surrounding healthy tissues (e.g., bladder and rectum), the prostate in the new treatment image needs to be accurately localized. Therefore, the effectiveness and efficiency of external beam radiotherapy highly depend on the accurate localization of the prostate. However, due to the low contrast of the prostate with its surrounding tissues (e.g., bladder), the unpredicted prostate motion, and the large appearance variations across different treatment days, it is challenging to segment the prostate in CT images. In this paper, the authors present a novel classification based segmentation method to address these problems.To segment the prostate, the proposed method first uses sparse representation based classification (SRC) to enhance the prostate in CT images by pixel-wise classification, in order to overcome the limitation of poor contrast of the prostate images. Then, based on the classification results, previous segmented prostates of the same patient are used as patient-specific atlases to align onto the current treatment image and the majority voting strategy is finally adopted to segment the prostate. In order to address the limitations of the traditional SRC in pixel-wise classification, especially for the purpose of segmentation, the authors extend SRC from the following four aspects: (1) A discriminant subdictionary learning method is proposed to learn a discriminant and compact representation of training samples for each class so that the discriminant power of SRC can be increased and also SRC can be applied to the large-scale pixel-wise classification. (2) The L1 regularized sparse coding is replaced by the elastic net in order to obtain a smooth and clear prostate boundary in the classification result. (3) Residue-based linear regression is incorporated to improve the classification performance and to extend SRC from hard classification to soft classification. (4) Iterative SRC is proposed by using context information to iteratively refine the classification results.The proposed method has been comprehensively evaluated on a dataset consisting of 330 CT images from 24 patients. The effectiveness of the extended SRC has been validated by comparing it with the traditional SRC based on the proposed four extensions. The experimental results show that our extended SRC can obtain not only more accurate classification results but also smoother and clearer prostate boundary than the traditional SRC. Besides, the comparison with other five state-of-the-art prostate segmentation methods indicates that our method can achieve better performance than other methods under comparison.The authors have proposed a novel prostate segmentation method based on the sparse representation based classification, which can achieve considerably accurate segmentation results in CT prostate segmentation.
| S-EPMC3477196 | biostudies-other
Project description:Accurate segmentation of the prostate from magnetic resonance (MR) images provides useful information for prostate cancer diagnosis and treatment. However, automated prostate segmentation from 3D MR images faces several challenges. The lack of clear edge between the prostate and other anatomical structures makes it challenging to accurately extract the boundaries. The complex background texture and large variation in size, shape and intensity distribution of the prostate itself make segmentation even further complicated. Recently, as deep learning, especially convolutional neural networks (CNNs), emerging as the best performed methods for medical image segmentation, the difficulty in obtaining large number of annotated medical images for training CNNs has become much more pronounced than ever. Since large-scale dataset is one of the critical components for the success of deep learning, lack of sufficient training data makes it difficult to fully train complex CNNs. To tackle the above challenges, in this paper, we propose a boundary-weighted domain adaptive neural network (BOWDA-Net). To make the network more sensitive to the boundaries during segmentation, a boundary-weighted segmentation loss is proposed. Furthermore, an advanced boundary-weighted transfer leaning approach is introduced to address the problem of small medical imaging datasets. We evaluate our proposed model on three different MR prostate datasets. The experimental results demonstrate that the proposed model is more sensitive to object boundaries and outperformed other state-of-the-art methods.
| S-EPMC7015773 | biostudies-literature
Project description:BackgroundDelineation of the target volume is a time-consuming task in radiotherapy treatment planning, yet essential for a successful treatment of cancers such as prostate cancer. To facilitate the delineation procedure, the paper proposes an intuitive approach for 3D modeling of the prostate by slice-wise best fitting ellipses.MethodsThe proposed estimate is initialized by the definition of a few control points in a new patient. The method is not restricted to particular image modalities but assumes a smooth shape with elliptic cross sections of the object. A training data set of 23 patients was used to calculate a prior shape model. The mean shape model was evaluated based on the manual contour of 10 test patients. The patient records of training and test data are based on axial T1-weighted 3D fast-field echo (FFE) sequences. The manual contours were considered as the reference model. Volume overlap (Vo), accuracy (Ac) (both ratio, range 0-1, optimal value 1) and Hausdorff distance (HD) (mm, optimal value 0) were calculated as evaluation parameters.ResultsThe median and median absolute deviation (MAD) between manual delineation and deformed mean best fitting ellipses (MBFE) was Vo (0.9 ± 0.02), Ac (0.81 ± 0.03) and HD (4.05 ± 1.3)mm and between manual delineation and best fitting ellipses (BFE) was Vo (0.96 ± 0.01), Ac (0.92 ± 0.01) and HD (1.6 ± 0.27)mm. Additional results show a moderate improvement of the MBFE results after Monte Carlo Markov Chain (MCMC) method.ConclusionsThe results emphasize the potential of the proposed method of modeling the prostate by best fitting ellipses. It shows the robustness and reproducibility of the model. A small sample test on 8 patients suggest possible time saving using the model.
| S-EPMC3933010 | biostudies-literature
Project description:Background and purposeIn radiotherapy, automatic organ-at-risk segmentation algorithms allow faster delineation times, but clinically relevant contour evaluation remains challenging. Commonly used measures to assess automatic contours, such as volumetric Dice Similarity Coefficient (DSC) or Hausdorff distance, have shown to be good measures for geometric similarity, but do not always correlate with clinical applicability of the contours, or time needed to adjust them. This study aimed to evaluate the correlation of new and commonly used evaluation measures with time-saving during contouring.Materials and methodsTwenty lung cancer patients were used to compare user-adjustments after atlas-based and deep-learning contouring with manual contouring. The absolute time needed (s) of adjusting the auto-contour compared to manual contouring was recorded, from this relative time-saving (%) was calculated. New evaluation measures (surface DSC and added path length, APL) and conventional evaluation measures (volumetric DSC and Hausdorff distance) were correlated with time-recordings and time-savings, quantified with the Pearson correlation coefficient, R.ResultsThe highest correlation (R = 0.87) was found between APL and absolute adaption time. Lower correlations were found for APL with relative time-saving (R = -0.38), for surface DSC with absolute adaption time (R = -0.69) and relative time-saving (R = 0.57). Volumetric DSC and Hausdorff distance also showed lower correlation coefficients for absolute adaptation time (R = -0.32 and 0.64, respectively) and relative time-saving (R = 0.44 and -0.64, respectively).ConclusionSurface DSC and APL are better indicators for contour adaptation time and time-saving when using auto-segmentation and provide more clinically relevant and better quantitative measures for automatically-generated contour quality, compared to commonly-used geometry-based measures.
| S-EPMC7807544 | biostudies-literature
Project description:Background and purposeWe developed an automatic method to segment cardiac substructures given a radiotherapy planning CT images to support epidemiological studies or clinical trials looking at cardiac disease endpoints after radiotherapy.Material and methodsWe used a most-similar atlas selection algorithm and 3D deformation combined with 30 detailed cardiac atlases. We cross-validated our method within the atlas library by evaluating geometric comparison metrics and by comparing cardiac doses for simulated breast radiotherapy between manual and automatic contours. We analyzed the impact of the number of cardiac atlas in the library and the use of manual guide points on the performance of our method.ResultsThe Dice Similarity Coefficients from the cross-validation reached up to 97% (whole heart) and 80% (chambers). The Average Surface Distance for the coronary arteries was less than 10.3 mm on average, with the best agreement (7.3 mm) in the left anterior descending artery (LAD). The dose comparison for simulated breast radiotherapy showed differences less than 0.06 Gy for the whole heart and atria, and 0.3 Gy for the ventricles. For the coronary arteries, the dose differences were 2.3 Gy (LAD) and 0.3 Gy (other arteries). The sensitivity analysis showed no notable improvement beyond ten atlases and the manual guide points does not significantly improve performance.ConclusionWe developed an automated method to contour cardiac substructures for radiotherapy CTs. When combined with accurate dose calculation techniques, our method should be useful for cardiac dose reconstruction of a large number of patients in epidemiological studies or clinical trials.
| S-EPMC7807574 | biostudies-literature
Project description:Radiation therapy (RT) is a curative treatment modality for localized prostate cancer. Over the past two decades, advances in technology and imaging have considerably changed RT in prostate cancer treatment. Treatment has evolved from 2-dimensional (2D) planning using X-ray fields based on pelvic bony landmarks to 3-dimensional (3D) conformal RT (CRT) which uses computed tomography (CT) based planning. Despite improvements with 3D-CRT, dose distributions often remained suboptimal with portions of the rectum and bladder receiving unacceptably high doses. In more recent years, intensity-modulated radiation therapy (IMRT) has become the standard of care to deliver external beam RT. IMRT uses multiple radiation beams of different shapes and intensities delivered from a wide range of angles to 'paint' the radiation dose onto the tumor. IMRT allows for a higher dose of radiation to be delivered to the prostate while reducing dose to surrounding organs. Multiple clinical trials have demonstrated improved cancer outcomes with dose escalation, but toxicities using 3D-CRT and escalated doses have been problematic. IMRT is a method to deliver dose escalated RT with more conformal dose distributions than 3D-CRT and has been associated with improved toxicity profiles. IMRT also appears to be the safest method to deliver hypofractionated RT and pelvic lymph node radiation. The purpose of this review is to summarize the technical aspects of IMRT planning and delivery, and to review the literature supporting the use of IMRT for prostate cancer.
| S-EPMC6043750 | biostudies-other
Project description:AIM:This article gives an overview on the current status of hypofractionated radiotherapy in the treatment of prostate cancer with a special focus on the applicability in routine use. METHODS:Based on a recently published systematic review the German Society of Radiation Oncology (DEGRO) expert panel added additional information that has become available since then and assessed the validity of the information on outcome parameters especially with respect to long-term toxicity and long-term disease control. RESULTS:Several large-scale trials on moderate hypofractionation with single doses from 2.4-3.4 Gy have recently finished recruiting or have published first results suggestive of equivalent outcomes although there might be a trend for increased short-term and possibly even long-term toxicity. Large phase 3 trials on extreme hypofractionation with single doses above 4.0 Gy are lacking and only very few prospective trials have follow-up periods covering more than just 2-3 years. CONCLUSION:Until the results on long-term follow-up of several well-designed phase 3 trials become available, moderate hypofractionation should not be used in routine practice without special precautions and without adherence to the highest quality standards and evidence-based dose fractionation regimens. Extreme hypofractionation should be restricted to prospective clinical trials.
| S-EPMC5203817 | biostudies-other
Project description:Radiotherapy (RT) datasets can suffer from variations in annotation of organ at risk (OAR) and target structures. Annotation standards exist, but their description for prostate targets is limited. This restricts the use of such data for supervised machine learning purposes as it requires properly annotated data. The aim of this work was to develop a modality independent deep learning (DL) model for automatic classification and annotation of prostate RT DICOM structures. Delineated prostate organs at risk (OAR), support- and target structures (gross tumor volume [GTV]/clinical target volume [CTV]/planning target volume [PTV]), along with or without separate vesicles and/or lymph nodes, were extracted as binary masks from 1854 patients. An image modality independent 2D InceptionResNetV2 classification network was trained with varying amounts of training data using four image input channels. Channel 1-3 consisted of orthogonal 2D projections from each individual binary structure. The fourth channel contained a summation of the other available binary structure masks. Structure classification performance was assessed in independent CT (n = 200 pat) and magnetic resonance imaging (MRI) (n = 40 pat) test datasets and an external CT (n = 99 pat) dataset from another clinic. A weighted classification accuracy of 99.4% was achieved during training. The unweighted classification accuracy and the weighted average F1 score among different structures in the CT test dataset were 98.8% and 98.4% and 98.6% and 98.5% for the MRI test dataset, respectively. The external CT dataset yielded the corresponding results 98.4% and 98.7% when analyzed for trained structures only, and results from the full dataset yielded 79.6% and 75.2%. Most misclassifications in the external CT dataset occurred due to multiple CTVs and PTVs being fused together, which was not included in the training data. Our proposed DL-based method for automated renaming and standardization of prostate radiotherapy annotations shows great potential. Clinic specific contouring standards however need to be represented in the training data for successful use. Source code is available at https://github.com/jamtheim/DicomRTStructRenamerPublic.
| S-EPMC8664152 | biostudies-literature
Project description:Radiotherapy is one of the most important treatment options for localized early-stage or advanced-stage prostate cancer (CaP). Radioresistance (relapse after radiotherapy) is a major challenge for the current radiotherapy. There is great interest in investigating mechanisms of radioresistance and developing novel treatment strategies to overcome radioresistance. MicroRNAs (miRNAs) are small, non-coding RNAs that regulate gene expression at the post-transcriptional level, participating in numerous physiological and pathological processes including cancer invasion, progression, metastasis and therapeutic resistance. Emerging evidence indicates that miRNAs play a critical role in the modulation of key cellular pathways that mediate response to radiation, influencing the radiosensitivity of the cancer cells through interplaying with other biological processes such as cell cycle checkpoints, apoptosis, autophagy, epithelial-mesenchymal transition and cancer stem cells. Here, we summarize several important miRNAs in CaP radiation response and then discuss the regulation of the major signalling pathways and biological processes by miRNAs in CaP radiotherapy. Finally, we emphasize on microRNAs as potential predictive biomarkers and/or therapeutic targets to improve CaP radiosensitivity.
| S-EPMC5595129 | biostudies-literature
Project description:External beam radiotherapy remains the primary treatment modality for localized prostate cancer. The radiobiology of prostate carcinoma lends itself to hypofractionation, with recent studies showing good outcomes with shorter treatment schedules. However, the ability to accurately deliver hypofractionated treatment is limited by current image-guided techniques. Magnetic resonance imaging is the main diagnostic tool for localized prostate cancer and its use in the therapeutic setting offers anatomical information to improve organ delineation. MR-guided radiotherapy, with daily re-planning, has shown early promise in the accurate delivery of radiotherapy. In this article, we discuss the shortcomings of current image-guidance strategies and the potential benefits and limitations of MR-guided treatment for prostate cancer. We also recount present experiences of MR-linac workflow and the opportunities afforded by this technology.
| S-EPMC7757637 | biostudies-literature