Automatic segmentation of the fetal brain is still challenging due to the
health state of fetal development, motion artifacts, and variability across
gestational ages, since existing methods rely on high-quality datasets of
healthy fetuses. In this work, we propose a novel cascade network called
CasUNext to enhance the accuracy and generalization of fetal brain MRI
segmentation. CasUNext incorporates depth-wise separable convolution, attention
mechanisms, and a two-step cascade architecture for efficient high-precision
segmentation. The first network localizes the fetal brain region, while the
second network focuses on detailed segmentation. We evaluate CasUNext on 150
fetal MRI scans between 20 to 36 weeks from two scanners made by Philips and
Siemens including axial, coronal, and sagittal views, and also validated on a
dataset of 50 abnormal fetuses. Results demonstrate that CasUNext achieves
improved segmentation performance compared to U-Nets and other state-of-the-art
approaches. It obtains an average Dice coefficient of 96.1% and mean
intersection over union of 95.9% across diverse scenarios. CasUNext shows
promising capabilities for handling the challenges of multi-view fetal MRI and
abnormal cases, which could facilitate various quantitative analyses and apply
to multi-site data.

提出了一种名为 CasUNext 的级联网络，用于提高胎儿脑 MRI 分割的准确性和泛化性，评估结果表明 CasUNext 在不同情景下取得了改进的分割性能，对于处理多视角胎儿 MRI 和异常情况具有很大潜力。

利用深度分离卷积和注意力机制增强广义胎儿脑磁共振图像分割的级连网络

Enhancing Generalized Fetal Brain MRI Segmentation using A Cascade  Network with Depth-wise Separable Convolution and Attention Mechanism

This paper introduces a novel pipeline designed to bring ultrasound (US)
plane pose estimation closer to clinical use for more effective navigation to
the standard planes (SPs) in the fetal brain. We propose a semi-supervised
segmentation model utilizing both labeled SPs and unlabeled 3D US volume
slices. Our model enables reliable segmentation across a diverse set of fetal
brain images. Furthermore, the model incorporates a classification mechanism to
identify the fetal brain precisely. Our model not only filters out frames
lacking the brain but also generates masks for those containing it, enhancing
the relevance of plane pose regression in clinical settings. We focus on fetal
brain navigation from 2D ultrasound (US) video analysis and combine this model
with a US plane pose regression network to provide sensorless proximity
detection to SPs and non-SPs planes; we emphasize the importance of proximity
detection to SPs for guiding sonographers, offering a substantial advantage
over traditional methods by allowing earlier and more precise adjustments
during scanning. We demonstrate the practical applicability of our approach
through validation on real fetal scan videos obtained from sonographers of
varying expertise levels. Our findings demonstrate the potential of our
approach to complement existing fetal US technologies and advance prenatal
diagnostic practices.

本文介绍了一种新型流水线，旨在将超声（US）平面姿态估计更接近临床应用，以实现对胎儿大脑中标准平面（SPs）的更有效导航。我们提出了一种半监督分割模型，利用标记的 SPs 和未标记的 3D US 体积切片，实现对多样化的胎儿大脑图像的可靠分割。此外，该模型还采用分类机制来精确定位胎儿大脑。我们的模型不仅能过滤掉缺少大脑的帧，还能为包含大脑的帧生成掩模，增强了平面姿态回归在临床环境中的相关性。我们侧重于从二维超声（US）视频分析进行胎儿大脑导航，并将该模型与 US 平面姿态回归网络相结合，以提供对 SPs 和非 SPs 平面的无传感器邻近检测。我们强调邻近检测对于指导超声波医生至 SPs 的重要性，相比传统方法，这种方法的优势在于允许在扫描过程中进行更早和更精确的调整。通过对由不同专业水平的超声波医生获得的真实胎儿扫描视频进行验证，我们证明了我们方法的实用可行性。我们的研究结果展示了我们方法在补充现有胎儿超声技术和推进产前诊断实践方面的潜力。

测量胎儿脑超声扫描中与标准平面的接近程度

Measuring proximity to standard planes during fetal brain ultrasound  scanning

Accurate segmentation of the fetal brain from Magnetic Resonance Image (MRI)
is important for prenatal assessment of fetal development. Although deep
learning has shown the potential to achieve this task, it requires a large fine
annotated dataset that is difficult to collect. To address this issue,
weakly-supervised segmentation methods with image-level labels have gained
attention, which are commonly based on class activation maps from a
classification network trained with image tags. However, most of these methods
suffer from incomplete activation regions, due to the low-resolution
localization without detailed boundary cues. To this end, we propose a novel
weakly-supervised method with image-level labels based on semantic features and
context information exploration. We first propose an Uncertainty-weighted
Multi-resolution Class Activation Map (UM-CAM) to generate high-quality
pixel-level supervision. Then, we design a Geodesic distance-based Seed
Expansion (GSE) method to provide context information for rectifying the
ambiguous boundaries of UM-CAM. Extensive experiments on a fetal brain dataset
show that our UM-CAM can provide more accurate activation regions with fewer
false positive regions than existing CAM variants, and our proposed method
outperforms state-of-the-art weakly-supervised methods with image-level labels.

本文提出了一种基于 UM-CAM 的新型弱监督学习方法，利用语义特征和上下文信息探测，解决了采用图像级标签的弱监督分割方法中出现的活跃区域不完整的问题，对于胎儿脑分割任务取得了良好的性能。