The paper introduces AWR-Net, a two‑stage framework that decouples anatomy and appearance to synthesize realistic 3D fetal brain ultrasound volumes from anatomical label maps. The first stage uses wavelet diffusion to generate volumes from atlas pairs in the wavelet domain, while the second stage applies residual refinement in the image domain to adapt to real ultrasound appearance. Experiments on real fetal brain ultrasound data show that AWR‑Net outperforms existing synthesis methods, improving metrics such as normalized cross‑correlation and Fréchet Inception Distance, and also enhances downstream segmentation, especially for severe abnormal cases.
By Yuhuan Lu, Sergio Valencia, Yuanji Zhang, Yuhao Huang, Camilo Jaimes, P. Ellen Grant, Davood Karimi
arXiv:2608. 04766v1 Announce Type: cross Abstract: A large number of infants with congenital anomalies are born each year globally, especially in areas with underdeveloped medical resources.
By Bin Pu, Jiewen Yang, Liwen Wang, Ying Tan, Guannan He, Xingbo Dong, Qika Lin, Jiarong Guo, Lixian Yang, Zuozhu Liu, Shengli Li, Kenli Li
The paper presents a two‑stage framework for prenatal congenital heart disease (CHD) screening that operates directly on whole fetal ultrasound studies. It first learns transferable frame representations via self‑supervised masked‑autoencoder pre‑training, then identifies cardiac frames with a disease‑robust module and aggregates them using a transformer‑based multiple instance learning model to produce a case‑level diagnosis. The approach achieves high performance (AUC 0.985, specificity 0.990) on an internal test set and, after label‑free CORAL adaptation, improves to an AUC of 0.944 on an external cohort, outperforming existing baselines.
By Mohamed Azzam, Ruobing Liu, Esther C. Ugwueke, Ziyang Xu, Shibiao Wan, Alex Foy, Abraham Zabih, Jason Christensen, Neil Hamill, Ling Li, Jieqiong Wang
arXiv:2606. 01293v1 Announce Type: cross Abstract: Accurate segmentation of fetal brain tissues in Magnetic Resonance Imaging (MRI) is critical for early diagnosis of congenital abnormalities and improving prenatal care.
By Ashiqur Rahman, Muhammad E. H. Chowdhury, Md. Abu Sayed, Md. Sharjis Ibne Wadud, Abu Naser Md. Arafat, Mehedi Hasan Prince
arXiv:2607. 20136v1 Announce Type: cross Abstract: Slice-to-volume reconstruction (SVR) is the standard method for obtaining high-resolution (HR) 3D fetal brain volumes from motion-corrupted 2D MRI slice stacks acquired in multiple orientations.
By Busra Bulut, Maik Dannecker, Thomas Sanchez, Sara Neves Silva, Steven Jia, Jean-Baptiste Ledoux, Leo Pomar, Joanna Sichitiu, Yvan Gomez, Meriam Koob, Vincent Dunet, Maria Deprez, Guillaume Auzias, Francois Rousseau, Jana Hutter, Daniel Rueckert, Meritxell Bach Cuadra
AnatoProto is a lightweight sequence‑level framework that adapts a frozen BiomedCLIP encoder for detecting the fetal abdominal circumference standard plane in low‑cost obstetric blind sweeps. It incorporates anatomy‑weighted spatial pooling, a within‑case prototype loss, a three‑stage cascade refinement, and a hybrid prediction head to address the highly imbalanced, short‑segment nature of the task. On the ACOUSLIC‑AI benchmark, AnatoProto achieves a test F1 of 67.72, surpassing the best foundation‑model baseline by 13.20 F1 and the best video temporal‑action‑detection baseline by 15.76 F1.
By Yuzhe Zhao