Professor Wang Ching-Wei, a leading expert in medical imaging technology and director of Taiwan Tech's Graduate Institute of Biomedical Engineering, has developed a new AI model that rapidly and accurately identifies and segments various types of lesions in CT scan images. Her team secured 3rd place in the 2024 International Medical 3D CT Image AI Competition, the Universal Lesion Segmentation '23 Challenge.

Professor Wang Ching-Wei (right) from Taiwan Tech's Institute of Biomedical Engineering, with master’s student Ting-Sheng Su (left).

The Universal 3D Lesion Segmentation AI Model, developed by Professor Wang and her team, precisely identifies lesions affecting the thorax and abdomen, including those in bones, the pancreas, kidneys, liver, colon, and lymph nodes. The model is tailored for thoracoabdominal CT images, assisting radiologists in 3D lesion annotation and addressing the issue of high labor costs associated with manual annotation.

3D lesion segmentation results: The red contour represents the baseline truth, the green contour represents the model's prediction.

Automated AI lesion segmentation for CT scans offers significant advantages over manual segmentation, such as improved efficiency, repeatability, accuracy, and standardization, leading to more precise quantitative analysis. Traditional manual annotation takes about 30 to 60 minutes per case, whereas Professor Wang’s model can process each 3D lesion in just 3.25 seconds [on the Grand Challenge platform server equipped with a single T4 GPU, and less than 2 seconds on a computer equipped with an RTX 4080].

Professor Wang and her team are proud of their third place at 2024 International Medical 3D CT image AI competition.

The team stood out among 632 participants in this year's Universal Lesion Segmentation '23 Challenge [ULS23], earning 3rd place. The ULS23 competition, held on the Grand Challenge platform, aims to promote research on universal lesion segmentation models in the 3D CT field. The competition provided a clinical test set of 39,500 3D CT lesion images, allowing participants to build and validate multi-category thoracoabdominal lesion models.

Video: https://www.youtube.com/watch?v=kPQLhCx9ViA
Contact: 
Professor Ching-Wei Wang
Director of Director of the Graduate Institute of Biomedical Engineering
Email: cweiwang@mail.ntust.edu.tw