arXiv AI

Measuring proximity to standard planes during fetal brain ultrasound scanning

This study introduces a pipeline that enhances ultrasound plane pose estimation for fetal brain imaging by providing continuous, real‑time proximity feedback to standard planes (SPs). It employs a semi‑supervised segmentation model achieving high mIoU scores on both SPs and non‑SPs, and integrates a classification step to filter out frames without the fetal brain. The system, validated on an NVIDIA Clara AGX edge device, runs at 39 Hz and has been tested on real scan videos from 17 sonographers, demonstrating its practical viability for clinical use.

arXiv Computer Vision

Aug 28

Anatomy-Guided Foundation Model Adaptation with Within-Case Prototype Supervision for Standard Plane Detection in Fetal Ultrasound Blind Sweeps

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

rag benchmarks

arXiv AI

Aug 10

Geometry-Aware Camera Localization for Bronchoscopy

arXiv:2608. 07116v1 Announce Type: cross Abstract: Camera localization in bronchoscopy remains a challenging problem due to stringent accuracy requirements, real-time constraints, and limited training data.

By Lumin Chen, Qingyao Tian, Jinpeng Li, Haoyu Jiang, Huai Liao, Xinyan Huang, Hongbin Liu, Dong Yi

llms computer-vision benchmarks

arXiv AI

Jul 22

FedCC: A Low-Resource Federated Adaptation of Foundation Models for Robust Corpus Callosum localization in Fetal Ultrasound Images

arXiv:2607. 18283v1 Announce Type: cross Abstract: Accurate localization of the corpus callosum (CC) in fetal ultrasound (US) images is crucial for the early identification of neurodevelopmental abnormalities.

By Alessandro Di Matteo, Sara Moccia, Giuseppe Rizzo, Gianpaolo Grisolia, Ricciarda Raffaelli, Lorenzo Vasciaveo, Francesco D'Antonio, Maria Chiara Fiorentino

computer-vision fine-tuning safety

arXiv Computer Vision

Sep 1

SCoPE-Reg: Efficient Rigid Ultrasound Slice-to-Volume Registration via State-Space Correlation and Closed-Form Pose Estimation

arXiv:2608.28715v1 Announce Type: cross Abstract: Ultrasound-guided interventions can require localization of an untracked 2D frame within a 3D anatomical reference. Rigid slice-to-volume registratio...

By Niklas Schwarz, Jens Kleesiek, Moritz Rempe

computer-vision benchmarks

arXiv Computer Vision

Aug 28

UniFLM: United Segmentation and Measurement on Fetal Limb Ultrasonic Image

The paper introduces UniFLM, a unified framework for segmenting and measuring fetal long bones in ultrasound images. It presents the Fetal Limb Bones (FLB) dataset with high‑quality annotations for the humerus, femur, tibia‑fibula, and radius‑ulna. UniFLM employs a Semantic‑Aware Skip Connection, a Positive Sampling strategy, and a Point Regression Mapping module to improve segmentation accuracy and bone length measurement, achieving superior performance over existing models on the FLB dataset.

By Zeen Zhou, Qiuhua Chen, Xiaojun Cao, Changmao Chen, Chao Sun, Bo Du

computer-vision benchmarks

arXiv AI

Aug 6

FUSEP: A Multi-Center Benchmark for Diverse Tasks in Early Pregnancy Fetal Ultrasound Screening

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

computer-vision fine-tuning benchmarks

arXiv AI

Jun 10

FADA: Accessible fetal ultrasound interpretation and annotation with a selectively distilled unified vision-language model

arXiv:2606. 11106v1 Announce Type: cross Abstract: A global shortage of trained sonographers limits prenatal ultrasound screening in low- and middle-income countries, where over half of pregnant women receive no skilled sonography.

By Mahmood Alzubaidi, Uzair Shah, Raden Muaz, Ines Abbes, Nader Mohammed, Abdullatif Magram, Khalid Alyafei, Mowafa Househ, Marco Agus

llms agents computer-vision fine-tuning efficiency multimodal safety

arXiv AI

Aug 18

Cross-Modal Ultrasound-MRI Learning for Fetal Brain Ventricular Volumetry and Abnormality Screening

arXiv:2608. 14763v1 Announce Type: cross Abstract: Assessment of ventriculomegaly (VM) on fetal brain ultrasound relies primarily on measuring lateral ventricular atrial width on standard planes, which is operator-dependent and may not fully reflect the overall ventricular enlargement.

By Yuhao Huang, Yuanji Zhang, Yuhuan Lu, Dong Ni, P. Ellen Grant, Davood Karimi

llms rag multimodal benchmarks safety

arXiv Computer Vision

Aug 25

Dense Structural Priors for Sparse Functional Landmark Localization in Surgical Videos

The paper presents a method for localizing functional surgical landmarks—specifically instrument tips and anchors—in surgical videos without requiring manual pixel-level mask annotations. It leverages vision foundation models, such as SAM 3, to generate dense structural priors through zero‑shot, point‑prompted masks, and refines landmark predictions with a lightweight, coarse‑to‑fine multi‑frame network. Experiments on 7,867 clips from 60 videos show that the approach achieves F1 scores of 72.4% for tip and 58.0% for anchor localization, with ablations confirming the benefits of structural priors and refinement stages.

By Chenyan Jing, Hao Ding, Lalithkumar Seenivasan, Jacob M. Delgado L\'opez, Mathias Unberath

computer-vision robotics

arXiv AI

Jun 30

SonoCLIP: Mask-Guided Region-Aware Vision-Language Pretraining for Fetal Ultrasound Analysis

arXiv:2606. 29586v1 Announce Type: cross Abstract: Vision-language foundation models have shown strong potential in medical image analysis.

By Hang Su, Chao Sun, Zhaofan Li, Wei Hu, Juhua Liu, Bo Du

computer-vision multimodal safety

arXiv Computer Vision

1d ago

Open ultrasound foundation model for robust segmentation and clinical measurement across heterogeneous settings

arXiv:2609.19230v1 Announce Type: new Abstract: Ultrasound is the most widely deployed imaging modality worldwide, yet clinical AI remains fragmented into narrow single-task models that fail when dev...

By Chao Qin, Fahad Shahbaz Khan, Salman Khan, Sarim Ather, Siddiq Anwar, Rao Muhammad Anwer, Shadab Khan

computer-vision benchmarks

arXiv AI

Jun 4

Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentation

arXiv:2606. 04705v1 Announce Type: cross Abstract: Semantic segmentation in medical imaging is a critical yet challenging task due to data scarcity and high variability across modalities.

By Amirhossein Movahedisefat, Amirreza Fateh, Mohammad Reza Mohammadi

rag computer-vision

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