The objectives of the MOUSIE project were early revised and adapted in light of the recent progress in the field of machine learning.
More specifically, the objectives of the tasks accomplished in the project are:
• Development of new machine learning frameworks for the automatic segmentation of the fetal skull in 3D US volumes. 3D US has the capacity to mitigate many of the limitations of 2D scans, reducing subjectivity, and improving diagnosis accuracy and reproducibility. However, despite the potential of volumetric data, it is necessary to develop new technological solutions for the efficient analysis, visualization and segmentation of the clinically relevant information in 3DUS. In this line, I have created different segmentation architectures able to automatically delineate the fetal skull from 3DUS volumes.
• Development of new frameworks for optimal volumetric data acquisition, visualization and navigation. Traditionally, sonographers have been trained to acquire optimal 2D planar views with the free-and US probe. However, the acquisition of new 3D data may be affected by the lack of experience with this new technology, reducing the quality of the data, or slowing down the acquisition process as compared to traditional 2D US. Additionally, 3D US imaging also poses new challenges, such as the efficient visualization and navigation of dense volumetric data. The creation of new tools to address these problems is key in for the successful integration of 3D sonography into the clinical practice.
• Creation of a new generation of 3D biometrics for the assessment of fetal head dismorphology. Traditionally, the assessment of fetal skull deformations was based on simple 2D biometrics manually extracted from single 2D planar views of the head. Due to this oversimplified approach, the early identification of potential structural anomalies is highly subjective, and operator-dependent, with the detection rates remaining below the recommended values. Thanks to the computational power provided by recent machine learning models, it is now possible to extract rich 3D anatomical information directly from 3D US volumes, allowing for a more accurate, robust, and objective analysis of the fetal anatomy.
• Development of new generative models for the 3D reconstruction of the fetal anatomy from 2D views. Despite the potential of 3D fetal biometry, its clinical impact can be notably hampered by two important factors: i) the lack of a significant number of cases to create statistically relevant anatomical models; ii) the limited access to 3D US transducers, especially in developing countries and remote areas. Aware of these limitations, another important part of the project was to investigate new technological solutions that contribute to the popularization of 3D-based fetal anatomical analysis, allowing for large-scale 3D-based biometric studies that include a wide and varied demographic representation. In this context, the project also investigated new computational solutions for the 3D reconstruction of the fetal skull from 2D US standard planes routinely acquired in the clinical practice.
