CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation (2307.04513v2)

Abstract: New lesion segmentation is essential to estimate the disease progression and therapeutic effects during multiple sclerosis (MS) clinical treatments. However, the expensive data acquisition and expert annotation restrict the feasibility of applying large-scale deep learning models. Since single-time-point samples with all-lesion labels are relatively easy to collect, exploiting them to train deep models is highly desirable to improve new lesion segmentation. Therefore, we proposed a coaction segmentation (CoactSeg) framework to exploit the heterogeneous data (i.e., new-lesion annotated two-time-point data and all-lesion annotated single-time-point data) for new MS lesion segmentation. The CoactSeg model is designed as a unified model, with the same three inputs (the baseline, follow-up, and their longitudinal brain differences) and the same three outputs (the corresponding all-lesion and new-lesion predictions), no matter which type of heterogeneous data is being used. Moreover, a simple and effective relation regularization is proposed to ensure the longitudinal relations among the three outputs to improve the model learning. Extensive experiments demonstrate that utilizing the heterogeneous data and the proposed longitudinal relation constraint can significantly improve the performance for both new-lesion and all-lesion segmentation tasks. Meanwhile, we also introduce an in-house MS-23v1 dataset, including 38 Oceania single-time-point samples with all-lesion labels. Codes and the dataset are released at https://github.com/ycwu1997/CoactSeg.

• The paper introduces a novel deep-learning framework that unifies heterogeneous datasets for effective new MS lesion segmentation.
• CoactSeg employs relation regularization and a three-element input (baseline, follow-up, and longitudinal differences) to streamline training and boost accuracy.
• The model achieves Dice scores of 63.82% and 72.32% on public and proprietary datasets, closely matching human neuro-radiologist performance.

A Critical Review of CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation

The paper "CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation" introduces a novel approach to enhance the segmentation of new lesions in multiple sclerosis (MS) through deep learning techniques. The CoactSeg framework strategically leverages heterogeneous datasets, including both two-time-point datasets with new-lesion annotations and single-time-point datasets with all-lesion annotations, to improve model training efficacy. This capability is particularly significant as it addresses the challenges associated with the costly and time-intensive data acquisition and expert annotation processes traditionally involved in MS lesion detection.

The CoactSeg model is designed to accommodate a uniform architectural input of three elements—baseline, follow-up, and longitudinal brain differences—and produces corresponding outputs for both all-lesion and new-lesion segmentations. This unification reduces model complexity and enhances scalability across varying datasets. Furthermore, the introduction of a relation regularization mechanism that leverages longitudinal relations among outputs significantly optimizes the learning process.

From a results perspective, the CoactSeg framework demonstrates noteworthy performance improvements. The model achieves a Dice score of 63.82% on the public MICCAI-21 dataset and 72.32% on the proprietary MS-23v1 dataset. When benchmarked against other methods, CoactSeg demonstrated superior segmentation accuracy for both new and all MS lesions. Its comparable performance to certain human neuro-radiologists emphasizes the potential of leveraging AI in clinical settings for MS monitoring.

The incorporation of an in-house dataset, MS-23v1, featuring 38 Oceania samples labeled with all-lesion annotations enhances the robustness of the model training. This dataset contributes to the diversification of available public MS datasets and enriches the training samples, potentially benefiting future research endeavors aimed at improving MS lesion segmentation.

The work makes significant strides in addressing the data scarcity issue by utilizing heterogeneous data more effectively. The strategic unification of data types into a single training regimen could inspire further research into domain adaptation techniques, where the goal would be to mitigate the domain shift inherent in using diverse datasets. Moreover, the results underscore the relevance of potential longitudinal constraints and their ability to harness temporal features for improved disease tracking and progression prediction.

Looking forward, future research could focus on expanding the capabilities of CoactSeg by integrating more granular longitudinal data to detect other MS lesion dynamics, such as shrinking or stable lesions. Moreover, the authors hint at the possibility of resolving domain gap challenges and ensuring fairness across various patient demographics, which is critical in any medical AI application.

In summation, the CoactSeg framework represents a promising contribution to the field of medical image processing for MS. By optimally leveraging heterogeneous data, it provides a viable pathway to enhance lesion detection capabilities, which could lead to more nuanced and automated diagnostic systems that support healthcare practitioners in managing MS.