Omid Haji Maghsoudi

Omid Haji Maghsoudi, Aimilia Gastounioti, Christopher Scott et al.

Breast density is an important risk factor for breast cancer that also affects the specificity and sensitivity of screening mammography. Current federal legislation mandates reporting of breast density for all women undergoing breast screening. Clinically, breast density is assessed visually using the American College of Radiology Breast Imaging Reporting And Data System (BI-RADS) scale. Here, we introduce an artificial intelligence (AI) method to estimate breast percentage density (PD) from digital mammograms. Our method leverages deep learning (DL) using two convolutional neural network architectures to accurately segment the breast area. A machine-learning algorithm combining superpixel generation, texture feature analysis, and support vector machine is then applied to differentiate dense from non-dense tissue regions, from which PD is estimated. Our method has been trained and validated on a multi-ethnic, multi-institutional dataset of 15,661 images (4,437 women), and then tested on an independent dataset of 6,368 digital mammograms (1,702 women; cases=414) for both PD estimation and discrimination of breast cancer. On the independent dataset, PD estimates from Deep-LIBRA and an expert reader were strongly correlated (Spearman correlation coefficient = 0.90). Moreover, Deep-LIBRA yielded a higher breast cancer discrimination performance (area under the ROC curve, AUC = 0.611 [95% confidence interval (CI): 0.583, 0.639]) compared to four other widely-used research and commercial PD assessment methods (AUCs = 0.528 to 0.588). Our results suggest a strong agreement of PD estimates between Deep-LIBRA and gold-standard assessment by an expert reader, as well as improved performance in breast cancer risk assessment over state-of-the-art open-source and commercial methods.

Omid Haji Maghsoudi, Mahdi Alizadeh

Studying animal locomotion improves our understanding of motor control and aids in the treatment of motor impairment. Mice are a premier model of human disease and are the model system of choice for much of basic neuroscience. High frame rates (250 Hz) are needed to quantify the kinematics of these running rodents. Manual tracking, especially for multiple markers, becomes time-consuming and impossible. Therefore, an automated method is necessary. We propose a method to track the paws of the animal in the following manner: first, segmenting all the possible paws based on color; second, classifying the segmented objects using a support vector machine (SVM) and neural network (NN); third, classifying the objects using the kinematic features of the running animal, coupled with texture features from earlier frames; and finally, detecting and handling collisions to assure the correctness of labelled paws. The proposed method is validated in sixty 1,000 frame video sequences (4 seconds) captured by four cameras from five mice. The total sensitivity for tracking of the front and hind paw is 99.70% using the SVM classifier and 99.76% using the NN classifier. In addition, we show the feasibility of 3D reconstruction using the four camera system.

Omid Haji Maghsoudi, Andrew Spence

Examining locomotion has improved our basic understanding of motor control and aided in treating motor impairment. Mice and rats are premier models of human disease and increasingly the model systems of choice for basic neuroscience. High frame rates (250 Hz) are needed to quantify the kinematics of these running rodents. Manual tracking, especially for multiple markers, becomes time-consuming and impossible for large sample sizes. Therefore, the need for automatic segmentation of these markers has grown in recent years. Here, we address this need by presenting a method to segment the markers using the SLIC superpixel method. The 2D coordinates on the image plane are projected to a 3D domain using direct linear transform (DLT) and a 3D Kalman filter has been used to predict the position of markers based on the speed and position of markers from the previous frames. Finally, a probabilistic function is used to find the best match among superpixels. The method is evaluated for different difficulties for tracking of the markers and it achieves 95% correct labeling of markers.

Omid Haji Maghsoudi

Wireless Capsule Endoscopy (WCE) is relatively a new technology to examine the entire GI trace. During an examination, it captures more than 55,000 frames. Reviewing all these images is time-consuming and prone to human error. It has been a challenge to develop intelligent methods assisting physicians to review the frames. The WCE frames are captured in 8-bit color depths which provides enough a color range to detect abnormalities. Here, superpixel based methods are proposed to segment five diseases including: bleeding, Crohn's disease, Lymphangiectasia, Xanthoma, and Lymphoid hyperplasia. Two superpixels methods are compared to provide semantic segmentation of these prolific diseases: simple linear iterative clustering (SLIC) and quick shift (QS). The segmented superpixels were classified into two classes (normal and abnormal) by support vector machine (SVM) using texture and color features. For both superpixel methods, the accuracy, specificity, sensitivity, and precision (SLIC, QS) were around 92%, 93%, 93%, and 88%, respectively. However, SLIC was dramatically faster than QS.

Omid Haji Maghsoudi, Annie Vahedipour Tabrizi, Benjamin Robertson et al.

Examining locomotion has improved our basic understanding of motor control and aided in treating motor impairment. Mice and rats are premier models of human disease and increasingly the model systems of choice for basic neuroscience. High frame rates (250 Hz) are needed to quantify the kinematics of these running rodents. Manual tracking, especially for multiple markers, becomes time-consuming and impossible for large sample sizes. Therefore, the need for automatic segmentation of these markers has grown in recent years. We propose two methods to segment and track these markers: first, using SLIC superpixels segmentation with a tracker based on position, speed, shape, and color information of the segmented region in the previous frame; second, using a thresholding on hue channel following up with the same tracker. The comparison showed that the SLIC superpixels method was superior because the segmentation was more reliable and based on both color and spatial information.