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Functional lung imaging is of great importance for the diagnosis and evaluation of lung diseases such as chronic obstructive pulmonary disease (COPD), asthma, and cystic fibrosis. Conventional methods often include inhaled hypopolarized gas or 100% oxygen as contrast agents. In recent years, high performance low field systems have shown great advantages for 1H lung MRI due to reduced susceptibility effects and improved vessel conspicuity. These allow possibilities to detect regional volume changes throughout the respiratory cycle.
Recently, under the collabration between MCL and Dynamic Imaging Science Center (DISC), the feasibility of image-based regional lung ventilation assessment from real-time low field MRI at 0.55T is studied, without requiring contrast agents, repetition, or breath holds. A sequence of MRI in the time series with 355ms/frame temporal resolution, 1.64 x 1.64 mm2 spatial resolution, and 15mm slice thickness, captures several consecutive respiratory cycles which consist of different respiratory states from exhalation to inhalation. To resolve the regional lung ventilation based on these acquired images, an unsupervised non-rigid image registration is applied to register the lungs from different respiratory states to the end-of-exhalation. Deformation field is extracted to study the regional ventilation. Specifically, a data-driven binarization algorithm for segmentation is firstly applied to the lung parenchyma area and vessels, separately. A frame-by-frame salient point extraction and matching are performed between the two adjacent frames to form pairs of landmarks. Finally, Jacobian determinant (JD) maps are generated using the calculated deformation fields after a landmark-based B-spline registration.
In the study, the regional lung ventilation is analyzed on three breathing patterns, including free breathing, deep breathing and force exhalation. The motion and volume change for deep breathing and forced exhalation are found to be larger than the free breathing case. [...]

Odometry is the process of using motion sensors to estimate the change in position of an object over time. It has been widely studied in the context of mobile robots, autonomous vehicles, drones, and other moving agents. Traditional odometry based on motion sensors such as Inertial Measurement Unit (IMU) and magnetometers is prone to error accumulation over time, known as odometry drift. Visual odometry makes use of camera images and/or point cloud scans collected over time to determine the position and orientation of the moving object. Several visual odometry systems that integrate monocular, stereo vision, point clouds and IMU have been developed for object localization.
We propose an unsupervised learning method for visual odometry from LiDAR point clouds called Green Point Cloud Odometry (GPCO). GPCO follows the traditional scan matching based approach to solve the odometry problem by incrementally estimating the motion between two consecutive point cloud scans. The GPCO method can be divided into four steps. First, a geometry-aware sampling method selects a small subset of points from the input point clouds. To do so, the eigen features of points in a local neighborhood are considered followed by random point sampling. Next, the 3D view surrounding the moving object is partitioned into four parts representing the front, rear, left and right-side view. The view-partitioning step divides the sampled points into four disjoint sets. The features of the sampled points are derived using the PointHop++ [1] method. Matching points between two consecutive point clouds are found in each view using nearest neighbor rule in the feature space. Finally, the motion between the two scans is estimated using Singular Value Decomposition (SVD). The motion is updated to the estimates from previous times and the process [...]

Given a point cloud set, the goal of semantic segmentation is to label every point as one of the semantic categories. Semantic segmentation of large-scale point clouds finds a wide range of real-world applications such as autonomous driving in an out-door environment and robotic navigation in an in- or out-door environment. As compared with the point cloud classification problem that often targets at small-scale objects, a high-performance point cloud semantic segmentation method demands a good understanding of the complex global structure as well as the local neighborhood of each point. Meanwhile, efficiency measured by computational complexity and memory complexity is important for practical real-time systems.

State-of-the-art point cloud classification and segmentation methods are based on deep learning. Raw point clouds captured by the LiDAR sensors are irregular and unordered. They cannot be directly processed by deep learning networks designed for 2D images. This problem was addressed by the pioneering work on the PointNet. PointNet and its follow-ups achieved impressive performance in small-scale point cloud classification and segmentation tasks, but they can’t be generalized to handle large-scale point cloud directly due to the memory and time constraints.

An efficient solution to semantic segmentation of large-scale indoor scene point clouds is proposed in this work. It is named GSIP (Green Segmentation of Indoor Point clouds) [1], and its performance is evaluated on a representative large-scale benchmark — the Stanford 3D Indoor Segmentation (S3DIS) dataset. GSIP has two novel components: 1) a room-style data pre-processing method that selects a proper subset of points for further processing, and 2) a new feature extractor which is extended from PointHop. For the former, sampled points of each room form an input unit. For the latter, the weaknesses of PointHop’s feature extraction when [...]

We are so happy to welcome a new graduate member of MCL, Mahtab Movahhedrad. Here is an interview with Mahtab:

Could you briefly introduce yourself and your research interests?

My name is Mahtab Movahhedrad. I’m from Tabriz, Iran. I started my Ph.D. at USC in spring 2021. I received my bachelor’s and my master’s degree from the University of Tabriz and, Tehran polytechnic respectively. I have been engaged in many different fields of study such as integrated electronics, photonics, metamaterials, and Fourier optics. Now, I plan to gear my Ph.D. Studies more towards signal processing and computer vision and do research in the same area.

What is your impression about MCL and USC?

I have been exploring my passion for computer vision and multimedia for some time now and I think MCL is the best research group in USC when it comes to this field. My motivation to join as a Ph.D. student to MCL is its diverse and outstanding research group in Multimedia. There are about 30 PhD students and several post-doctoral research fellows/visiting scholars from all over the world working in the lab. Doing my PhD at USC, among erudite professors and interested students would create a challenging and at the same time amicable atmosphere for me to broaden my knowledge and collaborate with other scientists.

What is your future expectation and plan in MCL?

My future expectation from the MCL is to learn about green AI and the chance to do research in this field. This method has the potential to accelerate global efforts to protect the environment and conserve resources. I find the concept quite interesting and can’t wait to explore more. I also hope to make lasting connections and effective collaborations with individuals [...]

The 2nd Ph.D. alumnus of MCL, Professor Kyoung Mu Lee of Seoul National University (SNU), has been appointed as the Editor-in-Chief (EiC) of the prestigious IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) from 2022 to 2024. TPAMI publishes articles on areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence. It is one of the premier journals in all of computer science. Its excellence, combined with its focus on computer vision and machine learning, positions it as one of IEEE’s flagship journals. Its impact factor is 16.39 in 2020.

Kyoung Mu Lee received his Ph. D. degree from the University of Southern California in 1993. He is currently a professor in the department of ECE and the director of the Interdisciplinary Graduate Program in Artificial Intelligence at Seoul National University (SNU). His primary research areas are Computer Vision and Machine Learning. He has served as an Editorial board member of many journals, including an Associate Editor in Chief (AEiC) of the IEEE TPAMI, Area Editor of the Computer Vision and Image Understanding (CVIU), and Associate Editor (AE) of the IEEE TPAMI, the Machine Vision Application (MVA), the IPSJ Transactions on Computer Vision and Applications (CVA), and the IEEE Signal Processing Letter. He is an Advisory Board Member of the Computer Vision Foundation (CVF) and an Editorial Advisory Board Member for Academic Press/Elsevier. He has served as a General co-Chair of the prestigious ICCV2019, ACM MM2018, ACCV2018, and an Area Chair of CVPR, ICCV, and ECCV many times. He was a Distinguished Lecturer of the Asia-Pacific Signal and Information Processing Association (APSIPA) for 2012-2013. He is currently serving as the president of the [...]