AI-driven processing of Correlative Multimodal Microscopy Images for enhanced analytical insights in disease and therapeutic research

Supervisor Organisation PhD Awarding Entity: phd location
SiriusXT Ltd
University College Dublin
SiriusXT, Dublin, Ireland

Research Focus

The project aims to revolutionize correlative microscopy by seamlessly integrating soft X-ray tomography (cryo-SXT) bridging resolutions and contrast mechanisms of light and electron microscopy. This will be achieved by developing experimental workflows and cutting-edge software capable of registering and segmenting two- and three-dimensional imaging data from multiple microscopy modalities.

Building upon the latest achievements in correlative microscopy, we will adapt current AI-powered image processing and quality assessment algorithms and develop new ones. This project involves hands-on experiments with other project CLEXM Partners, who operate leading disease research microscopy labs, to establish sample-handling workflows and conduct ground-breaking experiments to push the boundaries of scientific knowledge in biomedicine.


Correlative imaging data of biological specimens collected by CLEXM partners using cryo-SXT, cryo-ET, cryo-FIB/SEM, cryo-FM, cryo-3D-SIM, and hard X-ray microscopy will be shared with SiriusXT for their correlative integration and the development of segmentation workflows.

A survey of state-of-the-art correlative data registration and segmentation algorithms will be made. Algorithms and workflows for further development and incorporation into CLEXM software will be selected based on their performance and suitability. Sample preparation, handling protocols, correlative registration, and segmentation software development will be done in collaboration with the consortium partners.

Aim 1

Image Integration and analysis

Develop approaches to integrate data across imaging modalities and resolution scales building upon best practices used in electron tomography and fluorescence microscopy fields

Implement current or develop new multimodal 3D data segmentation algorithms to aid biological interpretation

Aim 2

Sample preparation and handling

Establish sample preparation and handling protocols to aid correlative data collection and integration

Aim 3


– Apply the developed software and sample handling protocols to aid biological interpretation in partner research

Pictures Attached

Fig. 1 (Left) Fluorescence image of a HeLa cell. (Right) Soft X-ray tomogram of the imaged cell with a 3D-segmented mitochondria shown in yellow.


PhD Researcher

Name: Shao Sen Chueh

University: University College Dublin

Supervisor’s Name: Prof Jeremy Simpson/Dr. Sergey Kapishnikov

Profile: ShaoSen Chueh completed his undergraduate studies at National Taiwan University, Department of Library and Information Science, with a focus on data analysis. He then pursued a master’s degree in computer science at the University of Nottingham, under the guidance of Professor Michael Pound, an expert of novel computer vision techniques. During his time in Nottingham, ShaoSen developed a passion for computer vision, deep learning, and their applications in medical image processing. This led him to embark on a one-year research project focused on multi-modality right ventricular segmentation. Prior to embarking on his doctoral studies at University College Dublin (UCD), ShaoSen honed his skills in 2D data processing by working as an embedded software engineer at Himax Technologies.

His research delves into the nexus of computer vision and deep learning, exploring their transformative potential in bioimage and medical image processing and analysis. His current research focuses on developing robust registration and segmentation algorithms for correlative fluorescence light and soft X-ray microscopy images. These efforts aim to enhance the accuracy and efficiency of image analysis, enabling deeper insights into biological and medical processes.

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