Prior Guided Deep Difference Meta-Learner for Fast Adaptation to Stylized Segmentation

When a pre-trained general auto-segmentation model is deployed at a new institution, a support framework in the proposed Prior-guided DDL network will learn the systematic difference between the model predictions and the final contours revised and approved by clinicians for an initial group of patients. The learned style feature differences are concatenated with the new patients (query) features and then decoded to get the style-adapted segmentations. The model is independent of practice styles and anatomical structures. It meta-learns with simulated style differences and does not need to be exposed to any real clinical stylized structures during training. Once trained on the simulated data, it can be deployed for clinical use to adapt to new practice styles and new anatomical structures without further training. To show the proof of concept, we tested the Prior-guided DDL network on six different practice style variations for three different anatomical structures. Pre-trained segmentation models were adapted from post-operative clinical target volume (CTV) segmentation to segment CTVstyle1, CTVstyle2, and CTVstyle3, from parotid gland segmentation to segment Parotidsuperficial, and from rectum segmentation to segment Rectumsuperior and Rectumposterior. The mode performance was quantified with Dice Similarity Coefficient (DSC). With adaptation based on only the first three patients, the average DSCs were improved from 78.6, 71.9, 63.0, 52.2, 46.3 and 69.6 to 84.4, 77.8, 73.0, 77.8, 70.5, 68.1, for CTVstyle1, CTVstyle2, and CTVstyle3, Parotidsuperficial, Rectumsuperior, and Rectumposterior, respectively, showing the great potential of the Priorguided DDL network for a fast and effortless adaptation to new practice style

An Anatomy-aware Framework for Automatic Segmentation of Parotid Tumor from Multimodal MRI

Magnetic Resonance Imaging (MRI) plays an important role in diagnosing the parotid tumor, where accurate segmentation of tumors is highly desired for determining appropriate treatment plans and avoiding unnecessary surgery. However, the task remains nontrivial and challenging due to ambiguous boundaries and various sizes of the tumor, as well as the presence of a large number of anatomical structures around the parotid gland that are similar to the tumor. To overcome these problems, we propose a novel anatomy-aware framework for automatic segmentation of parotid tumors from multimodal MRI. First, a Transformer-based multimodal fusion network PT-Net is proposed in this paper. The encoder of PT-Net extracts and fuses contextual information from three modalities of MRI from coarse to fine, to obtain cross-modality and multi-scale tumor information. The decoder stacks the feature maps of different modalities and calibrates the multimodal information using the channel attention mechanism. Second, considering that the segmentation model is prone to be disturbed by similar anatomical structures and make wrong predictions, we design anatomy-aware loss. By calculating the distance between the activation regions of the prediction segmentation and the ground truth, our loss function forces the model to distinguish similar anatomical structures with the tumor and make correct predictions. Extensive experiments with MRI scans of the parotid tumor showed that our PT-Net achieved higher segmentation accuracy than existing networks. The anatomy-aware loss outperformed state-of-the-art loss functions for parotid tumor segmentation. Our framework can potentially improve the quality of preoperative diagnosis and surgery planning of parotid tumors.Comment: under revie

Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline for Oropharyngeal Cancer Radiotherapy Treatment Guidance

Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information coupled to artificial intelligence (AI) approaches could improve clinical decision support for OPC by providing immediately actionable clinical rationale for adaptive RT planning. If tumors could be reproducibly segmented, rapid response could be classified, and prognosis could be reliably determined, overall patient outcomes would be optimized to improve the therapeutic index as a function of more risk-adapted RT volumes. Consequently, there is an unmet need for automated and reproducible imaging which can simultaneously segment tumors and provide predictive value for actionable RT adaptation. This dissertation primarily seeks to explore and optimize image processing, tumor segmentation, and patient outcomes in OPC through a combination of advanced imaging techniques and AI algorithms. In the first specific aim of this dissertation, we develop and evaluate mpMRI pre-processing techniques for use in downstream segmentation, response prediction, and outcome prediction pipelines. Various MRI intensity standardization and registration approaches were systematically compared and benchmarked. Moreover, synthetic image algorithms were developed to decrease MRI scan time in an effort to optimize our AI pipelines. We demonstrated that proper intensity standardization and image registration can improve mpMRI quality for use in AI algorithms, and developed a novel method to decrease mpMRI acquisition time. Subsequently, in the second specific aim of this dissertation, we investigated underlying questions regarding the implementation of RT-related auto-segmentation. Firstly, we quantified interobserver variability for an unprecedented large number of observers for various radiotherapy structures in several disease sites (with a particular emphasis on OPC) using a novel crowdsourcing platform. We then trained an AI algorithm on a series of extant matched mpMRI datasets to segment OPC primary tumors. Moreover, we validated and compared our best model\u27s performance to clinical expert observers. We demonstrated that AI-based mpMRI OPC tumor auto-segmentation offers decreased variability and comparable accuracy to clinical experts, and certain mpMRI input channel combinations could further improve performance. Finally, in the third specific aim of this dissertation, we predicted OPC primary tumor mid-therapy (rapid) treatment response and prognostic outcomes. Using co-registered pre-therapy and mid-therapy primary tumor manual segmentations of OPC patients, we generated and characterized treatment sensitive and treatment resistant pre-RT sub-volumes. These sub-volumes were used to train an AI algorithm to predict individual voxel-wise treatment resistance. Additionally, we developed an AI algorithm to predict OPC patient progression free survival using pre-therapy imaging from an international data science competition (ranking 1st place), and then translated these approaches to mpMRI data. We demonstrated AI models could be used to predict rapid response and prognostic outcomes using pre-therapy imaging, which could help guide treatment adaptation, though further work is needed. In summary, the completion of these aims facilitates the development of an image-guided fully automated OPC clinical decision support tool. The resultant deliverables from this project will positively impact patients by enabling optimized therapeutic interventions in OPC. Future work should consider investigating additional imaging timepoints, imaging modalities, uncertainty quantification, perceptual and ethical considerations, and prospective studies for eventual clinical implementation. A dynamic version of this dissertation is publicly available and assigned a digital object identifier through Figshare (doi: 10.6084/m9.figshare.22141871)

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

Segmentation of Parotid Gland Tumors Using Multimodal MRI and Contrastive Learning

Parotid gland tumor is a common type of head and neck tumor. Segmentation of the parotid glands and tumors by MR images is important for the treatment of parotid gland tumors. However, segmentation of the parotid glands is particularly challenging due to their variable shape and low contrast with surrounding structures. Recently deep learning has developed rapidly, which can handle complex problems. However, most of the current deep learning methods for processing medical images are still based on supervised learning. Compared with natural images, medical images are difficult to acquire and costly to label. Contrastive learning, as an unsupervised learning method, can more effectively utilize unlabeled medical images. In this paper, we used a Transformer-based contrastive learning method and innovatively trained the contrastive learning network with transfer learning. Then, the output model was transferred to the downstream parotid segmentation task, which improved the performance of the parotid segmentation model on the test set. The improved DSC was 89.60%, MPA was 99.36%, MIoU was 85.11%, and HD was 2.98. All four metrics showed significant improvement compared to the results of using a supervised learning model as a pre-trained model for the parotid segmentation network. In addition, we found that the improvement of the segmentation network by the contrastive learning model was mainly in the encoder part, so this paper also tried to build a contrastive learning network for the decoder part and discussed the problems encountered in the process of building