Zhaowen Qiu

Xi Jia, Joseph Bartlett, Tianyang Zhang et al.

Due to their extreme long-range modeling capability, vision transformer-based networks have become increasingly popular in deformable image registration. We believe, however, that the receptive field of a 5-layer convolutional U-Net is sufficient to capture accurate deformations without needing long-range dependencies. The purpose of this study is therefore to investigate whether U-Net-based methods are outdated compared to modern transformer-based approaches when applied to medical image registration. For this, we propose a large kernel U-Net (LKU-Net) by embedding a parallel convolutional block to a vanilla U-Net in order to enhance the effective receptive field. On the public 3D IXI brain dataset for atlas-based registration, we show that the performance of the vanilla U-Net is already comparable with that of state-of-the-art transformer-based networks (such as TransMorph), and that the proposed LKU-Net outperforms TransMorph by using only 1.12% of its parameters and 10.8% of its mult-adds operations. We further evaluate LKU-Net on a MICCAI Learn2Reg 2021 challenge dataset for inter-subject registration, our LKU-Net also outperforms TransMorph on this dataset and ranks first on the public leaderboard as of the submission of this work. With only modest modifications to the vanilla U-Net, we show that U-Net can outperform transformer-based architectures on inter-subject and atlas-based 3D medical image registration. Code is available at https://github.com/xi-jia/LKU-Net.

Xi Jia, Joseph Bartlett, Wei Chen et al.

Unsupervised image registration commonly adopts U-Net style networks to predict dense displacement fields in the full-resolution spatial domain. For high-resolution volumetric image data, this process is however resource-intensive and time-consuming. To tackle this problem, we propose the Fourier-Net, replacing the expansive path in a U-Net style network with a parameter-free model-driven decoder. Specifically, instead of our Fourier-Net learning to output a full-resolution displacement field in the spatial domain, we learn its low-dimensional representation in a band-limited Fourier domain. This representation is then decoded by our devised model-driven decoder (consisting of a zero padding layer and an inverse discrete Fourier transform layer) to the dense, full-resolution displacement field in the spatial domain. These changes allow our unsupervised Fourier-Net to contain fewer parameters and computational operations, resulting in faster inference speeds. Fourier-Net is then evaluated on two public 3D brain datasets against various state-of-the-art approaches. For example, when compared to a recent transformer-based method, named TransMorph, our Fourier-Net, which only uses 2.2\% of its parameters and 6.66\% of the multiply-add operations, achieves a 0.5\% higher Dice score and an 11.48 times faster inference speed. Code is available at \url{https://github.com/xi-jia/Fourier-Net}.

Gongning Luo, Kuanquan Wang, Jun Liu et al.

Efficient automatic segmentation of multi-level (i.e. main and branch) pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications. However, most existing methods concentrate only on main PA or branch PA segmentation separately and ignore segmentation efficiency. Besides, there is no public large-scale dataset focused on PA segmentation, which makes it highly challenging to compare the different methods. To benchmark multi-level PA segmentation algorithms, we organized the first \textbf{P}ulmonary \textbf{AR}tery \textbf{SE}gmentation (PARSE) challenge. On the one hand, we focus on both the main PA and the branch PA segmentation. On the other hand, for better clinical application, we assign the same score weight to segmentation efficiency (mainly running time and GPU memory consumption during inference) while ensuring PA segmentation accuracy. We present a summary of the top algorithms and offer some suggestions for efficient and accurate multi-level PA automatic segmentation. We provide the PARSE challenge as open-access for the community to benchmark future algorithm developments at \url{https://parse2022.grand-challenge.org/Parse2022/}.

Tianyang Zhang, Shaoming Zheng, Jun Cheng et al.

Generative models have been widely proposed in image recognition to generate more images where the distribution is similar to that of the real ones. It often introduces a discriminator network to differentiate the real data from the generated ones. Such models utilise a discriminator network tasked with differentiating style transferred data from data contained in the target dataset. However in doing so the network focuses on discrepancies in the intensity distribution and may overlook structural differences between the datasets. In this paper we formulate a new image-to-image translation problem to ensure that the structure of the generated images is similar to that in the target dataset. We propose a simple, yet powerful Structure-Unbiased Adversarial (SUA) network which accounts for both intensity and structural differences between the training and test sets when performing image segmentation. It consists of a spatial transformation block followed by an intensity distribution rendering module. The spatial transformation block is proposed to reduce the structure gap between the two images, and also produce an inverse deformation field to warp the final segmented image back. The intensity distribution rendering module then renders the deformed structure to an image with the target intensity distribution. Experimental results show that the proposed SUA method has the capability to transfer both intensity distribution and structural content between multiple datasets.

Yuetan Chu, Gongning Luo, Longxi Zhou et al.

Pulmonary artery-vein segmentation is crucial for disease diagnosis and surgical planning and is traditionally achieved by Computed Tomography Pulmonary Angiography (CTPA). However, concerns regarding adverse health effects from contrast agents used in CTPA have constrained its clinical utility. In contrast, identifying arteries and veins using non-contrast CT, a conventional and low-cost clinical examination routine, has long been considered impossible. Here we propose a High-abundant Pulmonary Artery-vein Segmentation (HiPaS) framework achieving accurate artery-vein segmentation on both non-contrast CT and CTPA across various spatial resolutions. HiPaS first performs spatial normalization on raw CT volumes via a super-resolution module, and then iteratively achieves segmentation results at different branch levels by utilizing the lower-level vessel segmentation as a prior for higher-level vessel segmentation. We trained and validated HiPaS on our established multi-centric dataset comprising 1,073 CT volumes with meticulous manual annotations. Both quantitative experiments and clinical evaluation demonstrated the superior performance of HiPaS, achieving an average dice score of 91.8% and a sensitivity of 98.0%. Further experiments showed the non-inferiority of HiPaS segmentation on non-contrast CT compared to segmentation on CTPA. Employing HiPaS, we have conducted an anatomical study of pulmonary vasculature on 11,784 participants in China (six sites), discovering a new association of pulmonary vessel anatomy with sex, age, and disease states: vessel abundance suggests a significantly higher association with females than males with slightly decreasing with age, and is also influenced by certain diseases, under the controlling of lung volumes.

Yuting Zhang, Karina V. Bunting, Asgher Champsi et al.

Cardiovascular diseases (CVDs) remain the foremost cause of mortality worldwide, a burden worsened by a severe deficit of healthcare workers. Artificial intelligence (AI) agents have shown potential to alleviate this gap via automated early detection and proactive screening, yet their clinical application remains limited by: 1) prompt-based clinical role assignment that relies on intrinsic model capabilities without domain-specific tool support; or 2) rigid sequential workflows, whereas clinical care often requires adaptive reasoning that orders specific tests and, based on their results, guides personalised next steps; 3) general and static knowledge bases without continuous learning capability; and 4) fixed unimodal or bimodal inputs and lack of on-demand visual outputs when further clarification is needed. In response, a multimodal framework, CardAIc-Agents, was proposed to augment models with external tools and adaptively support diverse cardiac tasks. Specifically, a CardiacRAG agent generated general plans from updatable cardiac knowledge, while the chief agent integrated tools to autonomously execute these plans and deliver decisions. To enable adaptive and case-specific customization, a stepwise update strategy was proposed to dynamically refine plans based on preceding execution results, once the task was assessed as complex. In addition, a multidisciplinary discussion tool was introduced to interpret challenging cases, thereby supporting further adaptation. When clinicians raised concerns, visual review panels were provided to assist final validation. Experiments across three datasets showed the efficiency of CardAIc-Agents compared to mainstream Vision-Language Models (VLMs), state-of-the-art agentic systems, and fine-tuned VLMs.

Yuting Zhang, Tiantian Geng, Luoying Hao et al.

Contemporary cardiovascular management involves complex consideration and integration of multimodal cardiac datasets, where each modality provides distinct but complementary physiological characteristics. While the effective integration of multiple modalities could yield a holistic clinical profile that accurately models the true clinical situation with respect to data modalities and their relatives weightings, current methodologies remain limited by: 1) the scarcity of patient- and time-aligned multimodal data; 2) reliance on isolated single-modality or rigid multimodal input combinations; 3) alignment strategies that prioritize cross-modal similarity over complementarity; and 4) a narrow single-task focus. In response to these limitations, a comprehensive multimodal dataset was curated for immediate application, integrating laboratory test results, electrocardiograms, and echocardiograms with clinical outcomes. Subsequently, a unified framework, Textual Guidance Multimodal fusion for Multiple cardiac tasks (TGMM), was proposed. TGMM incorporated three key components: 1) a MedFlexFusion module designed to capture the unique and complementary characteristics of medical modalities and dynamically integrate data from diverse cardiac sources and their combinations; 2) a textual guidance module to derive task-relevant representations tailored to diverse clinical objectives, including heart disease diagnosis, risk stratification and information retrieval; and 3) a response module to produce final decisions for all these tasks. Furthermore, this study systematically explored key features across multiple modalities and elucidated their synergistic contributions in clinical decision-making. Extensive experiments showed that TGMM outperformed state-of-the-art methods across multiple clinical tasks, with additional validation confirming its robustness on another public dataset.