Call for Applications for an M.Sc. Program on Liver Volumetry with Ultrasound Images (Application Deadline: 25/11/2021)
At the Department of Electronic and Telecommunication Engineering, The University of Moratuwa
Ultrasound imaging allows the organs inside the body, such as the liver, to be examined using high-frequency ultrasound waves. The limited fields of view and the comparatively high levels of noise in ultrasound images make measurements, such as organ volume calculation, a challenge. This work aims to automate the liver volume calculation via several ultrasound scans of the liver.
Firstly, the liver segmentation in the ultrasound images enables isolation of the liver region. Next, the stitching of ultrasound images enlarges the field of view for the whole liver region to be visible in one image, and detecting landmarks assists the process. Finally, the automatic liver volume calculation can take place.
- Liver Segmentation – Accuracy to be measured with intersection over union against the images segmented by the radiologists.
- Liver ultrasound image stitching – The accuracy metric is to be defined.
- Detecting landmarks when the image capture position is known.
- Automatic volume calculation based on the segmentation, and its accuracy is to be measured by making comparisons against the calculations made by the radiologists.
- Dr. Rukshani Liyanaarachchi, The University of Moratuwa
- Dr. Ranga Rodrigo, The University of Moratuwa
- Industry supervisors
Zone24x7 (Pvt.) Ltd. through the Distributed Ultrasound Visualization and Diagnostic System project.
year, full time
An ideal candidate would be one with an exceptional academic record with enthusiasm to engage in medical image processing research. Familiarity with medical imaging or medical image processing and deep networks would be welcome. Strong programming skills will be a definite advantage.
How to Apply:
Send in your CV with a half-page write-up on the aforementioned problem to rukshanil[at]uom.lk.Read More
A research paper titled “CeyMo: See More on Roads – A Novel Benchmark Dataset for Road Marking Detection” has been accepted to the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022. The research was conducted by Oshada Jayasinghe, Sahan Hemachandra, Damith Anhettigama, and Shenali Kariyawasam under the supervision of Dr. Peshala Jayasekara and Dr. Ranga Rodrigo.
The paper introduces a novel road marking benchmark dataset for road marking detection in advanced driver assistance systems and autonomous vehicles. Though understanding road markings is a fundamental perception task in autonomous vehicles, the development of robust road marking detection algorithms has been a challenging task in the research community due to diverse road, illumination, and traffic scenarios. The introduced dataset covers a wide variety of urban, sub-urban, and rural road scenarios with diverse illumination and weather conditions. The multiple annotation formats and the clear evaluation metrics with the evaluation script will promote novel road marking detection algorithms and direct comparison of them. Furthermore, the authors evaluate the approaches of utilizing instance segmentation-based and object detection-based neural network architectures for the road marking detection task, which will facilitate future research on road marking detection in challenging environments.
WACV is a top international conference in the field of computer vision and pattern recognition organized annually by the IEEE Computer Society and the Computer Vision Foundation. WACV 2022 will be held from 4th to 8th of January 2022 in Waikoloa, Hawaii, USA. This research was carried out as a final year project at the Department of Electronic and Telecommunication Engineering (ENTC), University of Moratuwa in collaboration with Creative Software Pvt. Ltd.
Nima L. Wickramasinghe from the Department of Electronic and Telecommunication Engineering (ENTC), together with his mentor Mohamed Athif from the Department of Biomedical Engineering, Boston University (previously an undergraduate at ENTC) has won the 3rd place in 3 categories (All-lead, 3-lead, 2-lead) in the Physionet/Computing in Cardiology Challenge 2021.
PhysioNet supports challenges, which invite participants from all over the world from various institutes to tackle clinically interesting questions that are either unsolved or not well-solved. In cooperation with the Computing in Cardiology conference, PhysioNet has been co-hosting a challenge annually. This year, the conference was held in Brno, Czech Republic from 12th to 15th of September in a hybrid manner. Computing in Cardiology (formerly Computers in Cardiology) is an international scientific conference that has been held annually since 1974. CinC provides a forum for scientists and professionals from the fields of medicine, physics, engineering, and computer science to discuss their current research in topics pertaining to computing in clinical cardiology and cardiovascular physiology.
This year’s challenge was to identify Cardiac abnormalities (26 scored) given the ECG data of the patients. Usually, the ECG data consists of the signals from the 12-leads. But, this year’s challenge focused on whether the same accuracy can be achieved using reduced-lead ECG data. Using a smaller number of leads would enable low-cost, portable, and user-friendly point of care devices.
The team (team NIMA) proposed a novel solution to tackle this Multi-label classification problem by creating a Deep convolutional neural network that used the time domain and frequency domain of the ECG signals to classify the 26 scored cardiac abnormalities. The results showed that reduced-lead ECG data can obtain almost the same accuracy obtained using all leads.
The team was able to obtain 3rd place in the All-lead, 3-lead, and 2-lead categories competing against 39 International teams. The 2nd place was obtained by team DSAIL_SNU, mainly from Seoul National University. And, the 1st place was obtained by team ISIBrno-AIMT, mainly from the Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic.
Remarks: The collaboration between Nima L. Wickramasinghe and Athif Mohamed was facilitated by the ScholarX program from the Sustainable Education Foundation. ScholarX is a 6-month program for Sri-Lankan undergraduates who would like to get free premium mentoring during their study period.
Link to challenge: https://physionetchallenges.org/2021/Read More
A team of undergraduate students (Team NFPUndercover) from the Department of Electronic and Telecommunication Engineering (ENTC) under the supervision of Dr. Chamira Edussooriya from ENTC, University of Moratuwa, became the 2nd runner up in the IEEE Video and Image Processing Cup (VIP Cup) 2021 competition at the 28th International Conference on Image Processing (ICIP) 2021. ICIP is an annual flagship conference of the IEEE Signal Processing Society which is one of the world’s premier associations for signal processing engineers, academics, and industry professionals. This year the conference was held from the 19th to 22nd of September 2021, virtually in Anchorage, Alaska, USA. The IEEE VIP Cup competition is one of the most prestigious competitions in video and image processing for undergraduate students. This year’s task was to develop a robust algorithm to estimate in-bed human poses under heavy occlusion caused by blankets and varying illumination conditions. This algorithm can be utilized for in-bed human behavior monitoring during sleep or rest state, which is crucial for prognostic, diagnostic, and treatment of many healthcare complications. Team NFPUndercover proposed a novel solution for this task leveraging multiple approaches from both computer vision and signal processing such as extreme occlusion-based data augmentation and label smoothening via knowledge distillation. Specifically, their approach is feasible to implement in a resource-constrained setup. Image Processing and Machine Vision is a branch of Artificial Intelligence that models and learns the image representations to autonomously accomplish downstream tasks such as Image Classification, Object Detection, Semantic Segmentation, etc. It utilizes advanced statistical and mathematical algorithms to make a machine see and understand the real world as humans do. This achievement by UoM places Sri Lanka at a higher position in the world of the image processing communityRead More
Team DigitX, a team of ten undergraduates from the Department of Electronic and Telecommunication Engineering (ENTC) under the supervision of Dr. Chamira Edussooriya from ENTC, University of Moratuwa, emerged as the champions of the Students Challenge at the Internal Conference on Autonomous Systems (ICAS) 2021 conference. The conference was held virtually in Montreal, Canada from August 11 to 13, 2021.
ICAS 2021 is the premier international forum organized by the IEEE Signal Processing Society (SPS) through IEEE SPS Autonomous Systems Initiative, for presenting the technological advances and research results in the fields of theoretical, experimental and applied autonomous systems. IEEE ICAS was held for the first time this year and it focused on providing an updated state-of-the-art over advanced signal processing theories and techniques that are relevant for developing autonomous systems.
The ICAS 2021 Students Challenge was on “unsupervised anomaly detection through self-aware autonomous systems”, to detect abnormal events during the navigation of a ground/ aerial vehicle. The team was able to develop an unsupervised learning algorithm with state-of the-art signal processing and machine learning techniques such as auto-encoders, recurrent neural networks for time-series data signal processing.
Based on the inertial measurement unit (IMU) sensor data, video data captured by the cameras of autonomous systems in several camera perspectives and light detection and ranging (LIDAR) sensor data (point clouds), the unsupervised model was trained to identify the normal situations that it saw during the training phase as normal and every other situation as abnormal (as per the challenge requirement a reference normal scenario was defined for each dataset). UoM won the first place while a team from Indian Institute of Technology (IIT), India and a team from CentralSupelec, France won first and second runners up, respectively.
An autonomous system is an artificial system capable of performing a set of tasks with a high degree of autonomy. Developing computing systems with advanced levels of autonomy has been a crucial task for decades in order to manage ever-increasing requirements in complexity. This achievement by UoM places Sri Lanka at a higher position in the world signal processing community.Read More
A team of undergraduate students (Team T- Cubed) from the Department of Electronic and Telecommunication Engineering (ENTC) under the supervision of Dr. Prathapasinghe Dharmawansa from ENTC, University of Moratuwa, won the first place in the IEEE Signal Processing Cup 2021 competition at the ICASSP 2021 conference.Read More
A Final Year Research Project from the 2015 Biomedical Engineering Batch from the Department of Electronic and Telecommunication Engineering (ENTC), University of Moratuwa secured the “MERIT Award” of the Manamperi Award (Engineering) 2020 for the “Most Outstanding Undergraduate Engineering Research Projects” in the Group Category.
The Research Project group comprised of 4 students from the Biomedical Engineering Specialization: K.G.G.L.A. De Silva, J.K.M.V. Perera, K.R.B. Wickremasinghe, and A.M. Naim, and was supervised by Dr. S.L. Kappel from the Department of Electronic and Telecommunication Engineering and Dr. T.D. Lalitharatne from the Department of Mechanical Engineering.
The research project was focused on “Designing a Cost-Effective Active Dry Contact sEMG Sensor System for Controlling a Bionic Hand”. The goal of the project was to develop a cost-effective neural interface for individual finger-based wireless interaction using sEMG technology. The device is aimed to be a novel wearable technology with many applications, mainly for Bionic Hand Control Systems. In addition to this achievement, the group has published 2 international conference papers at IEEE ICASSP 2020 and IEEE SMC 2020, and have also gotten an “Honorary Mention” at IEEE ComSoC Student Comp. 2019.
The “Manamperi Award” is awarded by the Sri Lanka Association for the Advancement of Science (SLAAS) each year for the Most Outstanding Undergraduate Engineering Research Project. It is open to all Engineering graduates who have graduated from a recognized university of Sri Lanka and completed a Final Year Research Project (Individual or Group) within the duration of the year this award is presented.Read More
The UoM team won the 1st runner up award in the signal processing cup (SP Cup) 2020 competition attached to the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), virtually held in Barcelona, Spain, from May 4 to 8, 2020. The winning team comprised of six undergrads of the Department of Electronic and Telecommunication Engineering (ENTC), and four undergrads of the Department of Computer Science and Engineering. Mr. Dumindu Tissera (a Ph.D. candidate of the ENTC) tutored and the team and Dr. Chamira Edussooriya (ENTC) supervised the team.
The SP Cup competition is an annual event organized by the IEEE signal processing society to encourage students to solve real-world problems using signal processing techniques. It is held as a sub-event of the ICASSP, which is the world’s largest and most comprehensive technical conference focused on signal processing and its applications, with more than 1500 research paper presentations each year.
This year’s SP Cup challenge was on “unsupervised abnormality detection by using intelligent and heterogeneous autonomous systems.” The UoM team employed a blend of signal processing techniques and state-of-the-art machine learning techniques, such as long short-term memory and conditional generative adversarial networks, to detect abnormalities (i.e., unusual or suspected events) using inertial measurement unit (IMU) sensor data and video data captured by the cameras of autonomous systems, e.g., drones. The proposed solution by the UoM team achieved 98% accuracy with IMU sensor data and 95% accuracy with video in detecting anomalies in the dataset provided for the competition. In the first round, the UoM team scored the highest among the participated teams from all around the world. In the final presentation, the team from the Technion – Israel Institute of Technology, Israel surpassed the UoM team.
Signal processing—” analyzing, modifying, and synthesizing signals such as sound, images, and scientific measurement”— is an important field in today’s information-rich world. This coveted achievement places the UOM within a substantial place in the signal processing community. The challenge is to continue excelling in the area of signal processing.Read More
The UoM team won the first runner-up award in the 2020 CASS COVID-19 Special Student Design Competition organized as an event of International Symposium on Circuits and Systems (ISCAS) 2020. ISCAS is the flagship conference of the IEEE Circuits and Systems society with more than 750 research paper presentations in each year. The winner of the competition is a team represented the University de Toulon, France, and the second runner-up award won by a team represented the University of Rhode Island, USA.
The project of the UoM team, Intelliscope: A Low-Cost AI-powered Stethoscope for Cardiovascular Disease Management in Resource-Constrained Environments, proposed a low-cost wireless stethoscope with an artificial intelligence back-end to help both semi-trained and expert medical professionals to diagnose cardiac abnormalities while wearing personal protective equipment or maintaining a safe distance from the patient. The UoM team employed a blend of state-of-the-art biomedical engineering, digital signal processing and machine learning techniques to develop the Intelliscope.Read More