CAT faces Python - Combining Deep-Learning with Traditional Segmentation

1836 

Abstract Submission 

Christian Gaser¹, Robert Dahnke¹

¹Jena University Hospital, Jena, Germany

Christian Gaser  
Jena University Hospital
Jena, Germany

Robert Dahnke  
Jena University Hospital
Jena, Germany

Deep learning (DL) techniques have shown great promise in the analysis and interpretation of medical images, offering unprecedented capabilities in identifying patterns and features indicative of various pathologies. However, while these approaches are remarkably accurate at recognising known conditions, they face significant challenges when confronted with data containing previously unseen or unexpected variations. This phenomenon is a significant barrier to clinical application, as it leads to a form of 'hallucination' where the algorithms generate false positive identifications or misinterpretations when encountering unfamiliar pathologies or abnormal variations [1,2]. Despite the growing interest in the application of DL to medical imaging, the lack of publications addressing this specific challenge is striking.

We use SynthSeg, a novel deep learning tool capable of segmenting brain scans of any contrast and resolution [3]. SynthSeg does not require retraining and has demonstrated robustness across different populations. It produces a label image and a parcellation (atlas) that are used to generate a skull-stripped and bias-corrected version of the original T1 image.
Our proposed hybrid segmentation approach integrates methods from the CAT12 toolbox [4], reimplemented in Python and C to eliminate any dependence on Matlab. Using an adaptive maximum a posteriori (AMAP) segmentation [5], we derived a partial volume estimation (PVE) segmentation from the processed image. This segmentation was then used to estimate cortical thickness and central surface area using projection-based thickness (PBT) [7].
Our comparative analysis involved evaluating our novel approach against (1) a conventional pipeline based on CAT12.8, and (2) a DL-based segmentation using the DL-based result to estimate cortical thickness and central surface.

The DL-based SynthSeg method exhibited a tendency to overestimate thickness values. This overestimation was accompanied by errors in sulcus reconstruction, particularly in regions such as the occipital lobe. In contrast, the hybrid approach showed superior performance, aligning closer to the estimates derived from the CAT12-based reference. In particular, the hybrid method showed improved accuracy in cortical thickness estimation and sulcus delineation, demonstrating its potential for more reliable and accurate neuroimaging analyses.

The development and application of our novel hybrid methodology addresses the challenges of DL-based segmentation of brain scans, particularly in clinical contexts involving multiple pathologies. By seamlessly integrating DL with traditional segmentation techniques, our approach mitigates the risk of hallucinations commonly associated with DL algorithms when faced with unexpected variations or pathologies. The observed improvements in accuracy and reliability, particularly in cortical thickness estimation and sulcus delineation, underscore the potential of our hybrid strategy to enhance the robustness of neuroimaging analyses. This hybrid fusion not only provides a more stable segmentation process, but also leverages the strengths of both DL and conventional methods, paving the way for more accurate and clinically relevant neuroimaging assessments.

Image Registration and Computational Anatomy ¹

Methods Development

Segmentation and Parcellation ²

Data analysis

Machine Learning

Morphometrics

Open-Source Code

Open-Source Software

Segmentation

STRUCTURAL MRI