Placenta volume measurement from 3D ultrasound images is critical for predicting pregnancy outcomes, and manual annotation is the gold standard. However, such manual annotation is expensive and time-consuming. Automated segmentation algorithms can often successfully segment the placenta, but these methods may not consistently produce robust segmentations suitable for practical use. Recently, inspired by the Segment Anything Model (SAM), deep learning-based interactive segmentation models have been widely applied in the medical imaging domain. These models produce a segmentation from visual prompts provided to indicate the target region, which may offer a feasible solution for practical use. However, none of these models are specifically designed for interactively segmenting 3D ultrasound images, which remain challenging due to the inherent noise of this modality. In this paper, we evaluate publicly available state-of-the-art 3D interactive segmentation models in contrast to a human-in-the-loop approach for the placenta segmentation task. The Dice score, normalized surface Dice, averaged symmetric surface distance, and 95-percent Hausdorff distance are used as evaluation metrics. We consider a Dice score of 0.95 a successful segmentation. Our results indicate that the human-in-the-loop segmentation model reaches this standard. Moreover, we assess the efficiency of the human-in-the-loop model as a function of the amount of prompts. Our results demonstrate that the human-in-the-loop model is both effective and efficient for interactive placenta segmentation. The code is available at \url{https://github.com/MedICL-VU/PRISM-placenta}.
Semantic contour detection is a challenging problem that is often met in medical imaging, of which placental image analysis is a particular example. In this paper, we investigate utero-placental interface (UPI) detection in 2D placental ultrasound images by formulating it as a semantic contour detection problem. As opposed to natural images, placental ultrasound images contain specific anatomical structures thus have unique geometry. We argue it would be beneficial for UPI detectors to incorporate global context modelling in order to reduce unwanted false positive UPI predictions. Our approach…
King’s College London（伦敦国王学院）；Technical University of Munich（慕尼黑工业大学）；City, University of London（伦敦城市大学）；Imperial College London（帝国理工学院）；FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）
Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in highly variable reference annotations, and (iii) the limited field-of-view of US prohibiting whole placenta assessment at late gestation. In this work, we address these three challenges with a multi-task learning approach that combines the classification of placental location (e.g., anterior, posterior) and semantic placenta segmentation in a single convolutional neural network. Through the classification task the mo…
An Automatic Detection Method for Hematoma Features in Placental Abruption Ultrasound Images Based on Few-Shot Learning
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology（南京信息工程大学电子与信息工程学院）；School of Automation, Wuxi University（无锡学院自动化学院）；Obstetrics and Gynecology Department, Suixian Maternal and Child Health Hospital（睢县妇幼保健院妇产科）
University College London（伦敦大学学院）；University Hospitals KU Leuven（鲁汶大学附属医院）
Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art automatic segmentation methods. However, fully automatic results may still need to be refined to become accurate and robust enough for clinical use. We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy. We use one CNN to obtain an initial automatic segmentation, on which user interact…
DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）；University of Cambridge（剑桥大学）
This paper addresses the task of detecting and localising fetal anatomical regions in 2D ultrasound images, where only image-level labels are present at training, i.e. without any localisation or segmentation information. We examine the use of convolutional neural network architectures coupled with soft proposal layers. The resulting network simultaneously performs anatomical region detection (classification) and localisation tasks. We generate a proposal map describing the attention of the network for a particular class. The network is trained on 85,500 2D fetal Ultrasound images and their as…
Graz University of Technology（格拉茨工业大学）；Ulm University（乌尔姆大学）；Shanghai Jiao Tong University（上海交通大学）；BioTechMed-Graz（格拉茨生物科技医疗）；Katharinenhospital（卡塔琳娜医院）
The manual outlining of hepatic metastasis in (US) ultrasound acquisitions from patients suffering from pancreatic cancer is common practice. However, such pure manual measurements are often very time consuming, and the results repeatedly differ between the raters. In this contribution, we study the in-depth assessment of an interactive graph-based approach for the segmentation for pancreatic metastasis in US images of the liver with two specialists in Internal Medicine. Thereby, evaluating the approach with over one hundred different acquisitions of metastases. The two physicians or the algor…
MedAtlas: Evaluating LLMs for Multi-Round, Multi-Task Medical Reasoning Across Diverse Imaging Modalities and Clinical Text
University of Science and Technology of China（中国科学技术大学）；Eastern Institute of Technology（东方理工大学）；Shanghai Jiao Tong University（上海交通大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）
Graz University of Technology（格拉茨技术大学）；BioTechMed（生物技术医学）；Shanghai Jiao Tong University（上海交通大学）；Katharinenhospital（卡塔琳娜医院）；Ulm University（乌尔姆大学）
Ultrasound (US) is the most commonly used liver imaging modality worldwide. Due to its low cost, it is increasingly used in the follow-up of cancer patients with metastases localized in the liver. In this contribution, we present the results of an interactive segmentation approach for liver metastases in US acquisitions. A (semi-) automatic segmentation is still very challenging because of the low image quality and the low contrast between the metastasis and the surrounding liver tissue. Thus, the state of the art in clinical practice is still manual measurement and outlining of the metastases…
EchoPilot: Training-Free Ultrasound Video Segmentation via Scale-Space Semantic Prompting and Reliability-Gated Memory
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Third Affiliated Hospital of Sun Yat-Sen University（中山大学第三附属医院）；Hong Kong Metropolitan University（香港 Metropolitan 大学）
UniUltra: Interactive Parameter-Efficient SAM2 for Universal Ultrasound Segmentation
School of Computer Science, University of Nottingham Ningbo China（Nottingham Ningbo中国大学计算机科学学院）；School of Computer Science, University of Nottingham, UK（Nottingham英国大学计算机科学学院）；School of Mathematical and Computational Sciences, Massey University（Massey大学数学与计算科学学院）；School of Data Science and Artificial Intelligence, Wenzhou University of Technology（温州科技大学数据科学与人工智能学院）；Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（香港科学院创新研究所）；The Hong Kong Polytechnic University（香港理工大学）
École Centrale de Nantes（南特中央理工学院）；Université de Nantes（南特大学）
We present an accurate, fast and efficient method for segmentation and muscle mask propagation in 3D freehand ultrasound data, towards accurate volume quantification. A deep Siamese 3D Encoder-Decoder network that captures the evolution of the muscle appearance and shape for contiguous slices is deployed. We uses it to propagate a reference mask annotated by a clinical expert. To handle longer changes of the muscle shape over the entire volume and to provide an accurate propagation, we devise a Bidirectional Long Short Term Memory module. Also, to train our model with a minimal amount of train…
TU Graz, Institute for Computer Graphics and Vision（格拉茨技术大学计算机图形与视觉研究所）；BioTechMed（生物技术医学研究联盟）；Shanghai Jiao Tong University, School of Mechanical Engineering（上海交通大学机械工程学院）；Katharinenhospital（卡塔琳娜医院）
Ultrasound (US) is the most commonly used liver imaging modality worldwide. It plays an important role in follow-up of cancer patients with liver metastases. We present an interactive segmentation approach for liver tumors in US acquisitions. Due to the low image quality and the low contrast between the tumors and the surrounding tissue in US images, the segmentation is very challenging. Thus, the clinical practice still relies on manual measurement and outlining of the tumors in the US images. We target this problem by applying an interactive segmentation algorithm to the US data, allowing th…
Ultrasound simulation based on ray tracing enables the synthesis of highly realistic images. It can provide an interactive environment for training sonographers as an educational tool. However, due to high computational demand, there is a trade-off between image quality and interactivity, potentially leading to sub-optimal results at interactive rates. In this work we introduce a deep learning approach based on adversarial training that mitigates this trade-off by improving the quality of simulated images with constant computation time. An image-to-image translation framework is utilized to tr…
Korea University（高丽大学）；Korea Basic Science Institute（韩国基础科学研究院）；Korea Artificial Organ Center（韩国人工器官中心）；ICT Convergence Technology for Health and Safety（健康安全信息通信融合技术）；Department of Electronics and Information Engineering（电子与信息工程系）；Bioimaging Research Team（生物成像研究团队）
Purpose: To acquire the real-time interactive temperature map for aqueous and adipose tissue, the problems of long acquisition and processing time must be addressed. To overcome these major challenges, this paper proposes a cascaded convolutional neural network (CNN) framework and multi-echo gradient echo (meGRE) with a single reference variable flip angle (srVFA). Methods: To optimize the echo times for each method, MR images are acquired using a meGRE sequence; meGRE images with two flip angles (FAs) and meGRE images with a single FA are acquired during the pretreatment and treatment stages,…
Purpose. Given the high level of expertise required for navigation and interpretation of ultrasound images, computational simulations can facilitate the training of such skills in virtual reality. With ray-tracing based simulations, realistic ultrasound images can be generated. However, due to computational constraints for interactivity, image quality typically needs to be compromised. Methods. We propose herein to bypass any rendering and simulation process at interactive time, by conducting such simulations during a non-time-critical offline stage and then learning image translation from cro…
Open ultrasound foundation model for robust segmentation and clinical measurement across heterogeneous settings
Mohamed bin Zayed Uni. of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Sheikh Tahnoon Bin Mohammed Medical City (STMC)（谢赫·塔赫农·本·穆罕默德医疗城）；King’s College Hospital London - Dubai（伦敦国王学院医院迪拜分院）；ADIA Lab（ADIA实验室）
Interactive simulation of ultrasound imaging greatly facilitates sonography training. Although ray-tracing based methods have shown promising results, obtaining realistic images requires substantial modeling effort and manual parameter tuning. In addition, current techniques still result in a significant appearance gap between simulated images and real clinical scans. Herein we introduce a novel content-preserving image translation framework (ConPres) to bridge this appearance gap, while maintaining the simulated anatomical layout. We achieve this goal by leveraging both simulated images with …
We present a meta-learning framework for interactive medical image registration. Our proposed framework comprises three components: a learning-based medical image registration algorithm, a form of user interaction that refines registration at inference, and a meta-learning protocol that learns a rapidly adaptable network initialization. This paper describes a specific algorithm that implements the registration, interaction and meta-learning protocol for our exemplar clinical application: registration of magnetic resonance (MR) imaging to interactively acquired, sparsely-sampled transrectal ult…
University Hospitals Leuven（鲁汶大学医院）；King’s College London（伦敦国王学院）；KU Leuven（鲁汶大学）；INESC TEC - Institute for Systems and Computer Engineering, Technology and Science（INESC TEC - 系统与计算机工程、技术与科学研究所）
Automatic medical image segmentation via convolutional neural networks (CNNs) has shown promising results. However, they may not always be robust enough for clinical use. Sub-optimal segmentation would require clinician's to manually delineate the target object, causing frustration. To address this problem, a novel interactive CNN-based segmentation framework is proposed in this work. The aim is to represent the CNN segmentation contour as B-splines by utilising B-spline explicit active surfaces (BEAS). The interactive element of the framework allows the user to precisely edit the contour in r…
We introduce a method for training neural networks to perform image or volume segmentation in which prior knowledge about the topology of the segmented object can be explicitly provided and then incorporated into the training process. By using the differentiable properties of persistent homology, a concept used in topological data analysis, we can specify the desired topology of segmented objects in terms of their Betti numbers and then drive the proposed segmentations to contain the specified topological features. Importantly this process does not require any ground-truth labels, just prior k…
