Incremental Learning With Selective Memory (ILSM): Towards Fast Prostate Localization for Image Guided Radiotherapy

Yaozong Gao,Yiqiang Zhan,Dinggang Shen IEEE 2014 IEEE transactions on medical imaging Vol.33 No.2

<P>Image-guided radiotherapy (IGRT) requires fast and accurate localization of the prostate in 3-D treatment-guided radiotherapy, which is challenging due to low tissue contrast and large anatomical variation across patients. On the other hand, the IGRT workflow involves collecting a series of computed tomography (CT) images from the same patient under treatment. These images contain valuable patient-specific information yet are often neglected by previous works. In this paper, we propose a novel learning framework, namely incremental learning with selective memory (ILSM), to effectively learn the patient-specific appearance characteristics from these patient-specific images. Specifically, starting with a population-based discriminative appearance model, ILSM aims to “personalize” the model to fit patient-specific appearance characteristics. The model is personalized with two steps: backward pruning that discards obsolete population-based knowledge and forward learning that incorporates patient-specific characteristics. By effectively combining the patient-specific characteristics with the general population statistics, the incrementally learned appearance model can localize the prostate of a specific patient much more accurately. This work has three contributions: 1) the proposed incremental learning framework can capture patient-specific characteristics more effectively, compared to traditional learning schemes, such as pure patient-specific learning, population-based learning, and mixture learning with patient-specific and population data; 2) this learning framework does not have any parametric model assumption, hence, allowing the adoption of any discriminative classifier; and 3) using ILSM, we can localize the prostate in treatment CTs accurately (DSC ~ 0.89) and fast ( ~ 4 s), which satisfies the real-world clinical requirements of IGRT.</P>

Dynamic Control Cycle Speed Limit Strategy for Improving Traffic Operation at Freeway Bottlenecks

Yaozong Zhang,Minghui Ma,Shidong Liang 대한토목학회 2021 KSCE JOURNAL OF CIVIL ENGINEERING Vol.25 No.2

In recent years, variable speed limit (VSL) has been used to decrease the total travel time and alleviate the traffic congestion on freeway. To improve traffic efficiency, we proposed a novel VSL strategy considering dynamic control cycle in this study. An extension of the cell transmission model was used to depict the traffic characteristics under VSL control, and the VSL strategy was designed to provide multiple control cycles. A probability formula was developed and used to determine the optimal quantity of control cycles in the VSL strategy. An objective optimization function was formulated with the aim of minimizing total travel time. A sensitivity analysis was applied to compare different control strategies under a variety of road bottleneck structures by both the numerical analysis and simulation experiments. The results show that dynamic control cycle VSL strategy outperformed other control scenarios and effectively reduced traffic congestion duration and enhanced the service level of freeway network.

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

Gao, Yaozong,Shao, Yeqin,Lian, Jun,Wang, Andrew Z.,Chen, Ronald C.,Shen, Dinggang IEEE 2016 IEEE transactions on medical imaging Vol.35 No.6

<P>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 non-local 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.</P>

Semi-Automatic Segmentation of Prostate in CT Images via Coupled Feature Representation and Spatial-Constrained Transductive Lasso

Yinghuan Shi,Yaozong Gao,Shu Liao,Daoqiang Zhang,Yang Gao,Dinggang Shen IEEE 2015 IEEE transactions on pattern analysis and machine Vol.37 No.11

<P>Conventional learning-based methods for segmenting prostate in CT images ignore the relations among the low-level features by assuming all these features are independent. Also, their feature selection steps usually neglect the image appearance changes in different local regions of CT images. To this end, we present a novel semi-automatic learning-based prostate segmentation method in this article. For segmenting the prostate in a certain treatment image, the radiation oncologist will be first asked to take a few seconds to manually specify the first and last slices of the prostate. Then, prostate is segmented with the following two steps: (i) Estimation of 3D prostate-likelihood map to predict the likelihood of each voxel being prostate by employing the coupled feature representation, and the proposed Spatial-COnstrained Transductive LassO (SCOTO); (ii) Multi-atlases based label fusion to generate the final segmentation result by using the prostate shape information obtained from both planning and previous treatment images. The major contribution of the proposed method mainly includes: (i) incorporating radiation oncologist's manual specification to aid segmentation, (ii) adopting coupled features to relax previous assumption of feature independency for voxel representation, and (iii) developing SCOTO for joint feature selection across different local regions. The experimental result shows that the proposed method outperforms the state-of-the-art methods in a real-world prostate CT dataset, consisting of 24 patients with totally 330 images, all of which were manually delineated by the radiation oncologist for performance evaluation. Moreover, our method is also clinically feasible, since the segmentation performance can be improved by just requiring the radiation oncologist to spend only a few seconds for manual specification of ending slices in the current treatment CT image.</P>

Bayesian Hybrid State Estimation for Unequal-length Batch Processes with Incomplete Observations

Guoli Ji,Yaozong Wang,Shunyi Zhao,Yunlong Liu,Kangkang Zhang,Bin Yao,Sun Zhou 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.6

This paper investigates state estimation problem for batch processes with unequal-length batches as wellas incomplete observations. A Bayesian hybrid state estimation method is proposed based on two dimensional (2D)correlations of states. The states of equal-length segment of time are estimated according to both within-a-batchand batch-to-batch correlations, and the states of unequal-length segment are obtained according to the correlationswithin the batch. In this way, the batch process states can be achieved in both equal-length and unequal-lengthsituations, of which the latter one is a more general case. In order to approximate state distribution of nonlinearsystem and to deal with the problem of incomplete observations, particle filter (PF) is employed. The proposedmethod shows its superiority with a nonlinear system and a gas-phase reaction process. Compared to a typicalexisting method, the proposed method provides better estimation accuracy in the situation of equal-length batches,also it shows less sensitivity to incomplete observations.

Hierarchical Lung Field Segmentation With Joint Shape and Appearance Sparse Learning

Yeqin Shao,Yaozong Gao,Yanrong Guo,Yonghong Shi,Xin Yang,Dinggang Shen IEEE 2014 IEEE transactions on medical imaging Vol.33 No.9

<P>Lung field segmentation in the posterior-anterior (PA) chest radiograph is important for pulmonary disease diagnosis and hemodialysis treatment. Due to high shape variation and boundary ambiguity, accurate lung field segmentation from chest radiograph is still a challenging task. To tackle these challenges, we propose a joint shape and appearance sparse learning method for robust and accurate lung field segmentation. The main contributions of this paper are: 1) a robust shape initialization method is designed to achieve an initial shape that is close to the lung boundary under segmentation; 2) a set of local sparse shape composition models are built based on local lung shape segments to overcome the high shape variations; 3) a set of local appearance models are similarly adopted by using sparse representation to capture the appearance characteristics in local lung boundary segments, thus effectively dealing with the lung boundary ambiguity; 4) a hierarchical deformable segmentation framework is proposed to integrate the scale-dependent shape and appearance information together for robust and accurate segmentation. Our method is evaluated on 247 PA chest radiographs in a public dataset. The experimental results show that the proposed local shape and appearance models outperform the conventional shape and appearance models. Compared with most of the state-of-the-art lung field segmentation methods under comparison, our method also shows a higher accuracy, which is comparable to the inter-observer annotation variation.</P>

Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

Yanrong Guo,Yaozong Gao,Dinggang Shen Institute of Electrical and Electronics Engineers 2016 IEEE transactions on medical imaging Vol.35 No.4

<P>Automatic and reliable segmentation of the prostate is an important but difficult task for various clinical applications such as prostate cancer radiotherapy. The main challenges for accurate MR prostate localization lie in two aspects: (1) inhomogeneous and inconsistent appearance around prostate boundary, and (2) the large shape variation across different patients. To tackle these two problems, we propose a new deformable MR prostate segmentation method by unifying deep feature learning with the sparse patch matching. First, instead of directly using handcrafted features, we propose to learn the latent feature representation from prostate MR images by the stacked sparse auto-encoder (SSAE). Since the deep learning algorithm learns the feature hierarchy from the data, the learned features are often more concise and effective than the handcrafted features in describing the underlying data. To improve the discriminability of learned features, we further refine the feature representation in a supervised fashion. Second, based on the learned features, a sparse patch matching method is proposed to infer a prostate likelihood map by transferring the prostate labels from multiple atlases to the new prostate MR image. Finally, a deformable segmentation is used to integrate a sparse shape model with the prostate likelihood map for achieving the final segmentation. The proposed method has been extensively evaluated on the dataset that contains 66 T2-wighted prostate MR images. Experimental results show that the deep-learned features are more effective than the handcrafted features in guiding MR prostate segmentation. Moreover, our method shows superior performance than other state-of-the-art segmentation methods. Index Terms-Deformable</P>

Nonlocal atlas-guided multi-channel forest learning for human brain labeling : Nonlocal atlas-guided multi-channel forest learning

Ma, Guangkai,Gao, Yaozong,Wu, Guorong,Wu, Ligang,Shen, Dinggang Wiley Blackwell (John Wiley Sons) 2016 Medical physics Vol.43 No.2

Automated segmentation of dental CBCT image with prior-guided sequential random forests : Automated segmentation of dental CBCT image

Wang, Li,Gao, Yaozong,Shi, Feng,Li, Gang,Chen, Ken-Chung,Tang, Zhen,Xia, James J.,Shen, Dinggang Published for the American Association of Physicis 2016 Medical physics Vol. No.

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning.

Guo, Yanrong,Gao, Yaozong,Shao, Yeqin,Price, True,Oto, Aytekin,Shen, Dinggang Published for the American Association of Physicis 2014 Medical physics Vol.41 No.7

<P>Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation.</P>