Detecting Anatomical Landmarks for Fast Alzheimer’s Disease Diagnosis

Zhang, Jun,Gao, Yue,Gao, Yaozong,Munsell, Brent C.,Shen, Dinggang Institute of Electrical and Electronics Engineers 2016 IEEE transactions on medical imaging Vol.35 No.12

<P>Structural magnetic resonance imaging (MRI) is a very popular and effective technique used to diagnose Alzheimer's disease (AD). The success of computer-aided diagnosis methods using structural MRI data is largely dependent on the two time-consuming steps: 1) nonlinear registration across subjects, and 2) brain tissue segmentation. To overcome this limitation, we propose a landmark-based feature extraction method that does not require nonlinear registration and tissue segmentation. In the training stage, in order to distinguish AD subjects from healthy controls (HCs), group comparisons, based on local morphological features, are first performed to identify brain regions that have significant group differences. In general, the centers of the identified regions become landmark locations (or AD landmarks for short) capable of differentiating AD subjects from HCs. In the testing stage, using the learned AD landmarks, the corresponding landmarks are detected in a testing image using an efficient technique based on a shape-constrained regression-forest algorithm. To improve detection accuracy, an additional set of salient and consistent landmarks are also identified to guide the AD landmark detection. Based on the identified AD landmarks, morphological features are extracted to train a support vector machine (SVM) classifier that is capable of predicting the AD condition. In the experiments, our method is evaluated on landmark detection and AD classification sequentially. Specifically, the landmark detection error (manually annotated versus automatically detected) of the proposed landmark detector is 2.41mm, and our landmark-based AD classification accuracy is 83.7%. Lastly, the AD classification performance of our method is comparable to, or even better than, that achieved by existing region-based and voxel-based methods, while the proposed method is approximately 50 times faster.</P>

Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

Wu, Guorong,Kim, Minjeong,Wang, Qian,Munsell, Brent C.,Shen, Dinggang IEEE 2016 IEEE Transactions on Biomedical Engineering Vol.63 No.7

<P>Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked autoencoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework, image registration experiments were conducted on 7.0-T brain MR images. In all experiments, the results showed that the new image registration framework consistently demonstrated more accurate registration results when compared to state of the art.</P>

Hierarchical and symmetric infant image registration by robust longitudinal-example-guided correspondence detection : Hierarchical and symmetric infant image registration

Wu, Yao,Wu, Guorong,Wang, Li,Munsell, Brent C.,Wang, Qian,Lin, Weili,Feng, Qianjin,Chen, Wufan,Shen, Dinggang Wiley (John WileySons) 2015 Medical physics Vol.42 No.7

<P>To investigate anatomical differences across individual subjects, or longitudinal changes in early brain development, it is important to perform accurate image registration. However, due to fast brain development and dynamic tissue appearance changes, it is very difficult to align infant brain images acquired from birth to 1-yr-old.</P>

Domain Transfer Learning for MCI Conversion Prediction

Cheng, Bo,Liu, Mingxia,Zhang, Daoqiang,Munsell, Brent C.,Shen, Dinggang IEEE 2015 IEEE Transactions on Biomedical Engineering Vol.62 No.7

<P>Machine learning methods have successfully been used to predict the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD), by classifying MCI converters (MCI-C) from MCI nonconverters (MCI-NC). However, most existing methods construct classifiers using data from one particular target domain (e.g., MCI), and ignore data in other related domains (e.g., AD and normal control (NC)) that may provide valuable information to improve MCI conversion prediction performance. To address is limitation, we develop a novel domain transfer learning method for MCI conversion prediction, which can use data from both the target domain (i.e., MCI) and auxiliary domains (i.e., AD and NC). Specifically, the proposed method consists of three key components: 1) a domain transfer feature selection component that selects the most informative feature-subset from both target domain and auxiliary domains from different imaging modalities; 2) a domain transfer sample selection component that selects the most informative sample-subset from the same target and auxiliary domains from different data modalities; and 3) a domain transfer support vector machine classification component that fuses the selected features and samples to separate MCI-C and MCI-NC patients. We evaluate our method on 202 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) that have MRI, FDG-PET, and CSF data. The experimental results show the proposed method can classify MCI-C patients from MCI-NC patients with an accuracy of 79.4%, with the aid of additional domain knowledge learned from AD and NC.</P>

The influence of surgeon volume on outcomes after pelvic exenteration for a gynecologic cancer

Randa J. Jalloul,Randa J. Jalloul,Mark F. Munsell,Shannon N. Westin,Pedro T. Ramirez,Michael Frumovitz,Pamela T. Soliman 대한부인종양학회 2018 Journal of Gynecologic Oncology Vol.29 No.5

Objective: To determine the effect of surgeon experience on intraoperative, postoperative and long-term outcomes among patients undergoing pelvic exenteration for gynecologic cancer. Methods: This was a retrospective analysis of all women who underwent exenteration for a gynecologic malignancy at MD Anderson Cancer Center, between January 1993 and June 2013. A logistic regression was used to model the relationship between surgeon experience (measured as the number of exenteration cases performed by the surgeon prior to a given exenteration) and operative outcomes and postoperative complications. Cox proportional hazards regression was used to model survival outcomes. Results: A total of 167 exenterations were performed by 19 surgeons for cervix (78, 46.7%), vaginal (43, 25.8%), uterine (24, 14.4%), vulvar (14, 8.4%) and other cancer (8, 4.7%). The most common procedure was total pelvic exenteration (69.4%), incontinent urinary diversion (63.5%) and vertical rectus abdominis musculocutaneous reconstruction (42.5%). Surgical experience was associated with decreased estimated blood loss (p<0.001), intraoperative transfusion (p=0.009) and a shorter length of stay (p=0.03). No difference was noted in the postoperative complication rate (p=0.12–0.95). More surgeon experience was not associated with overall or disease specific survival: OS (hazard ratio [HR]=1.02; 95% confidence interval [CI]=0.97–1.06; p=0.46) and DSS (HR=1.01; 95% CI=0.97–1.04; p=0.66), respectively. Conclusion: Patients undergoing exenteration by more experienced surgeons had improvement in intraoperative factors such as estimated blood loss, transfusion rates and length of stay. No difference was seen in postoperative complication rates, overall or disease specific survival.