While invasive procedures such as thoracoscopy can be used for tissue biopsy in the chest, they often increase the patient’s burden and may result in poor clinical outcomes. As a less invasive alternative, tissue samples can be collected through bronchoscopy. However, this requires a detailed map of the airway to navigate toward the target lesion. Conventional pixel intensity-based airway segmentation methods suffer from high false-positive rates (FPR) and often fail to detect small peripheral airways. To overcome these limitations, deep learning (DL)-based approaches have been proposed, resulting in improved scores. Nevertheless, they still exhibit limited sensitivity for fine airways, and the incompleteness of ground-truth labels for supervised learning poses additional challenges. In this study, we propose an Encoder-Guided Attention U-Net to enhance the sensitivity of airway detection in chest CT images. The proposed model is capable of detecting deeper and finer airway branches even under incomplete supervision. It achieved state-of-the-art (SOTA) performance in terms of Tree Detection Ratio (TDR) and Branch Detection Ratio (BDR) in the long-term validation phase of the ATM’22 Challenge. In this study, we are collaborating with the Department of Respiratory Medicine at Yangsan Pusan National University Hospital to develop a new image-guided method based on deep learning that can provide easy and accurate guidance for accessing peripheral lesions, aiming to overcome the limitations of peripheral lung lesion tissue biopsy and successfully perform early lung cancer tissue examination.

Lung cancer is one of the most prevalent and deadly types of cancer, and early diagnosis followed by timely treatment is critically important for improving patient outcomes. In some cases, lesions may not be clearly visible in CT images, which is why PET-CT scans are sequentially acquired to provide a more comprehensive diagnosis. PET imaging highlights regions of high metabolic activity, which helps identify potential malignancies. However, determining whether these areas actually represent cancerous tissue requires expert interpretation, typically performed by experienced nuclear medicine physicians. This diagnostic process is time-consuming, labor-intensive, and subject to inter-observer variability. To address these challenges, deep learning (DL)-based computer-aided diagnostic tools are gaining attention for their ability to automatically detect and segment cancerous lesions in PET-CT images. In this study, we propose a U-Net-based DL algorithm that automatically extracts lung cancer regions from PET-CT scans. The proposed framework includes image registration to handle multimodal data, ground truth preprocessing (mask pre-processing), and PET-CT axial alignment, along with post-processing steps to enhance sensitivity.

The demand for pathological examinations is increasing, but the management of pathological slides is challenging due to issues such as loss and damage. Therefore, large medical institutions are transitioning to digital pathology using scanners. Digitized pathological images through scanners have ultra-high resolution, and the whole-slide imaging method requires large storage capacity, reaching gigabytes per slide. Therefore, the development of compression techniques beyond JPEG2000 is necessary to store multiple sample images in the long term. Compression techniques can reduce server costs for storage capacity, as well as decrease transmission and loading times of image data, thereby enhancing diagnostic efficiency. In this study, we aim to improve the compression ratio of pathological images while minimizing the loss of image quality by applying state-of-the-art deep learning techniques such as instant NGP or coordinate-based MLP. Additionally, we intend to develop standardized image file formats using AI-based compression technology to aid in generating training data for image interpretation and disease prediction in the future. This project is supported by the Seegene Medical Foundation.

When imaging the inside of a patient's throat, it is often difficult to visually confirm the exact location of the vocal cords due to surrounding structures. In order to assist with diagnosis, a research project is underway to develop an algorithm that can predict the location of the vocal cords even when they are not directly visible. We are collaborating with the Departments of Anesthesiology and Pain Medicine at Pusan National University Yangsan Hospital and Dongguk University Ilsan Hospital for this study.