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检索条件"任意字段=8th International Conference on Medical Image Computing and Computer-Assisted Intervention"

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Masked Frequency Consistency for Domain-Adaptive Semantic Segmentation of Laparoscopic images 26th

Masked Frequency Consistency for Domain-Adaptive Semantic Se...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Zhao, Xinkai Hayashi, Yuichiro Oda, Masahiro Kitasaka, Takayuki Mori, Kensaku Nagoya Univ Grad Sch Informat Nagoya Aichi Japan Nagoya Univ Informat Strategy Off Informat & Commun Nagoya Aichi Japan Aichi Inst Technol Dept Informat Sci Toyota Japan Nagoya Univ Ctr Informat Technol Nagoya Aichi Japan Natl Inst Informat Res Ctr Med Bigdata Tokyo Japan

ISBN: (纸本)9783031439063;9783031439070

Semantic segmentation of laparoscopic images is an important issue for intraoperative guidance in laparoscopic surgery. However, acquiring and annotating laparoscopic datasets is labor-intensive, which limits the research on this topic. In this paper, we tackle the Domain-Adaptive Semantic Segmentation (DASS) task, which aims to train a segmentation network using only computer-generated simulated images and unlabeled real images. To bridge the large domain gap between generated and real images, we propose a Masked Frequency Consistency (MFC) module that encourages the network to learn frequency-related information of the target domain as additional cues for robust recognition. Specifically, MFC randomly masks some high-frequency information of the image to improve the consistency of the network's predictions for low-frequency images and real images. We conduct extensive experiments on existing DASS frameworks with our MFC module and show performance improvements. Our approach achieves comparable results to fully supervised learning method on the CholecSeg8K dataset without using any manual annotation. the code is available at ***/MoriLabNU/MFC.

关键词： Unsupervised Domain Adaptation Laparoscopic image Semantic Segmentation

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UXDiff: Synthesis of X-Ray image from Ultrasound Coronal image of Spine with Diffusion Probabilistic Network 26th

UXDiff: Synthesis of X-Ray Image from Ultrasound Coronal Ima...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Zhou, Yihao Wu, Chonglin Wang, Xinyi Zheng, Yongping Hong Kong Polytech Univ Dept Biomed Engn Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Smart Ageing Hong Kong Peoples R China

ISBN: (纸本)9783031439957;9783031439964

X-ray radiography with measurement of the Cobb angle is the gold standard for scoliosis diagnosis. However, cumulative exposure to ionizing radiation risks the health of patients. As a radiation-free alternative, imaging of scoliosis using 3D ultrasound scanning has recently been developed for the assessment of spinal deformity. Although these coronal ultrasound images of the spine can provide angle measurement comparable to X-rays, not all spinal bone features are visible. Diffusion probabilistic models (DPMs) have recently emerged as high-fidelity image generation models in medical imaging. To enhance the visualization of bony structures in coronal ultrasound images, we proposed UX-Diffusion, the first diffusion-based model for translating ultrasound coronal images to X-ray-like images of the human spine. To mitigate the underestimation in angle measurement, we first explored using ultrasound curve angle (UCA) to approximate the distribution of X-ray under Cobb angle condition in the reverse process. We then presented an angle embedding transformer module, establishing the angular variability conditions in the sampling stage. the quantitative results on the ultrasound and X-ray pair dataset achieved the state-of-the-art performance of high-quality X-ray generation and showed superior results in comparison with other reported methods. this study demonstrated that the proposed UX-diffusion method has the potential to convert coronal ultrasound image of spine into X-ray image for better visualization.

关键词： Diffusion model image-to-image translation image synthesis Ultrasound imaging Cobb angle Scoliosis

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Simulation-Based Parameter Optimization for Fetal Brain MRI Super-Resolution Reconstruction 26th

Simulation-Based Parameter Optimization for Fetal Brain MRI ...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： de Dumast, Priscille Sanchez, thomas Lajous, Helene Cuadra, Meritxell Bach Lausanne Univ Hosp CHUV Dept Radiol Lausanne Switzerland Univ Lausanne UNIL Lausanne Switzerland CIBM Ctr Biomed Imaging Lausanne Switzerland

ISBN: (纸本)9783031439896;9783031439902

Tuning the regularization hyperparameter a in inverse problems has been a longstanding problem. this is particularly true in the case of fetal brain magnetic resonance imaging, where an isotropic high-resolution volume is reconstructed from motion-corrupted low-resolution series of two-dimensional thick slices. Indeed, the lack of ground truth images makes challenging the adaptation of a to a given setting of interest in a quantitative manner. In this work, we propose a simulationbased approach to tune a for a given acquisition setting. We focus on the influence of the magnetic field strength and availability of input low-resolution images on the ill-posedness of the problem. Our results show that the optimal a, chosen as the one maximizing the similarity with the simulated reference image, significantly improves the super-resolution reconstruction accuracy compared to the generally adopted default regularization values, independently of the selected reconstruction pipeline. Qualitative validation on clinical data confirms the importance of tuning this parameter to the targeted clinical image setting. the simulated data and their reconstructions are available at https://zenodo. org/record/8123677.

关键词： Magnetic resonance imaging (MRI) fetal brain MRI Super-resolution reconstruction (SRR) parameter optimization image synthesis

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Deep Mutual Distillation for Semi-supervised medical image Segmentation 1

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Xie, Yushan Yin, Yuejia Li, Qingli Wang, Yan East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai 200241 Peoples R China

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

In this paper, we focus on semi-supervised medical image segmentation. Consistency regularization methods such as initialization perturbation on two networks combined with entropy minimization are widely used to deal with the task. However, entropy minimization-based methods force networks to agree on all parts of the training data. For extremely ambiguous regions, which are common in medical images, such agreement may be meaningless and unreliable. To this end, we present a conceptually simple yet effective method, termed Deep Mutual Distillation (DMD), a high-entropy online mutual distillation process, which is more informative than a low-entropy sharpened process, leading to more accurate segmentation results on ambiguous regions, especially the outer branches. Furthermore, to handle the class imbalance and background noise problem, and learn a more reliable consistency between the two networks, we exploit the Dice loss to supervise the mutual distillation. Extensive comparisons with all state-of-the-art on LA and ACDC datasets show the superiority of our proposed DMD, reporting a significant improvement of up to 1.15% in terms of Dice score when only 10% of training data are labelled in LA. We compare DMD with other consistency-based methods with different entropy guidance to support our assumption. Extensive ablation studies on the chosen temperature and loss function further verify the effectiveness of our design. the code is publicly available at https://***/SilenceMonk/Dual- MutualDistillation.

关键词： Semi-supervised learning Segmentation Knowledge distillation Consistency regularization

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Cross-Modulated Few-Shot image Generation for Colorectal Tissue Classification 1

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Kumar, Amandeep Bhunia, Ankan Kumar Narayan, Sanath Cholakkal, Hisham Anwer, Rao Muhammad Laaksonen, Jorma Khan, Fahad Shahbaz MBZUAI Masdar City U Arab Emirates Aalto Univ Espoo Finland Technol Innovat Inst Masdar City U Arab Emirates Linkoping Univ Linkoping Sweden

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

In this work, we propose a few-shot colorectal tissue image generation method for addressing the scarcity of histopathological training data for rare cancer tissues. Our few-shot generation method, named XM-GAN, takes one base and a pair of reference tissue images as input and generates high-quality yet diverse images. Within our XM-GAN, a novel controllable fusion block densely aggregates local regions of reference images based on their similarity to those in the base image, resulting in locally consistent features. To the best of our knowledge, we are the first to investigate few-shot generation in colorectal tissue images. We evaluate our few-shot colorectral tissue image generation by performing extensive qualitative, quantitative and subject specialist (pathologist) based evaluations. Specifically, in specialist-based evaluation, pathologists could differentiate between our XM-GAN generated tissue images and real images only 55% time. Moreover, we utilize these generated images as data augmentation to address the few-shot tissue image classification task, achieving a gain of 4.4% in terms ofmean accuracy over the vanilla few-shot classifier. Code: https://***/VIROBO-15/XM-GAN.

关键词： Few-shot image generation Cross Modulation

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Do We Really Need that Skip-Connection? Understanding Its Interplay with Task Complexity 26th

Do We Really Need that Skip-Connection? Understanding Its In...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Kamath, Amith Willmann, Jonas Andratschke, Nicolaus Reyes, Mauricio Univ Bern ARTORG Ctr Biomed Engn Res Bern Switzerland Univ Zurich Univ Zurich Hosp Dept Radiat Oncol Zurich Switzerland Paul Scherrer Inst Ctr Proton Therapy Villigen Switzerland Bern Univ Hosp Dept Radiat Oncol Inselspital Bern Switzerland Univ Bern Bern Switzerland

ISBN: (纸本)9783031439001;9783031439018

the U-Net architecture has become the preferred model used for medical image segmentation tasks. Since its inception, several variants have been proposed. An important component of the U-Net architecture is the use of skip-connections, said to carry over image details on its decoder branch at different scales. However, beyond this intuition, not much is known as to what extent skip-connections of the U-Net are necessary, nor what their interplay is in terms of model robustness when they are subjected to different levels of task complexity. In this study, we analyzed these questions using three variants of the U-Net architecture (the standard U-Net, a "No-Skip" U-Net, and an Attention-Gated U-Net) using controlled experiments on varying synthetic texture images and evaluated these findings on three medical image data sets. We measured task complexity as a function of texture-based similarities between foreground and background distributions. Using this scheme, our findings suggest that the benefit of employing skip-connections is small for low-to-medium complexity tasks, and its benefit appears only when the task complexity becomes large. We report that such incremental benefit is non-linear, with the Attention-Gated U-Net yielding larger improvements. Furthermore, we find that these benefits also bring along robustness degradations on clinical data sets, particularly in out-of-domain scenarios. these results suggest a dependency between task complexity and the choice/design of noise-resilient skip-connections, indicating the need for careful consideration while using these skip-connections.

关键词： image segmentation U-Net robustness

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Detecting the Sensing Area of a Laparoscopic Probe in Minimally Invasive Cancer Surgery 26th

Detecting the Sensing Area of a Laparoscopic Probe in Minima...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Huang, Baoru Hu, Yicheng Nguyen, Anh Giannarou, Stamatia Elson, Daniel S. Imperial Coll London Hamlyn Ctr Robot Surg London England Imperial Coll London Dept Surg & Canc London England Univ Liverpool Dept Comp Sci Liverpool Merseyside England

ISBN: (纸本)9783031439957;9783031439964

In surgical oncology, it is challenging for surgeons to identify lymph nodes and completely resect cancer even with pre-operative imaging systems like PET and CT, because of the lack of reliable intraoperative visualization tools. Endoscopic radio-guided cancer detection and resection has recently been evaluated whereby a novel tethered laparoscopic gamma detector is used to localize a preoperatively injected radiotracer. this can both enhance the endoscopic imaging and complement preoperative nuclear imaging data. However, gamma activity visualization is challenging to present to the operator because the probe is nonimaging and it does not visibly indicate the activity origination on the tissue surface. Initial failed attempts used segmentation or geometric methods, but led to the discovery that it could be resolved by leveraging high-dimensional image features and probe position information. To demonstrate the effectiveness of this solution, we designed and implemented a simple regression network that successfully addressed the problem. To further validate the proposed solution, we acquired and publicly released two datasets captured using a custom-designed, portable stereo laparoscope system. through intensive experimentation, we demonstrated that our method can successfully and effectively detect the sensing area, establishing a new performance benchmark. Code and data are available at https://***/br0202/Sensing area ***.

关键词： Laparoscopic image-guided intervention Minimally Invasive Surgery Detection of Sensing Area

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Domain Adaptation for medical image Segmentation Using Transformation-Invariant Self-training 26th

Domain Adaptation for Medical Image Segmentation Using Trans...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Ghamsarian, Negin Tejero, Javier Gamazo Marquez-Neila, Pablo Wolf, Sebastian Zinkernagel, Martin Schoeffmann, Klaus Sznitman, Raphael Univ Bern Ctr AI Med Fac Med Bern Switzerland Inselspital Bern Dept Ophthalmol Bern Switzerland Klagenfurt Univ Dept Informat Technol Klagenfurt Austria

ISBN: (纸本)9783031439063;9783031439070

Models capable of leveraging unlabelled data are crucial in overcoming large distribution gaps between the acquired datasets across different imaging devices and configurations. In this regard, self-training techniques based on pseudo-labeling have been shown to be highly effective for semi-supervised domain adaptation. However, the unreliability of pseudo labels can hinder the capability of self-training techniques to induce abstract representation from the unlabeled target dataset, especially in the case of large distribution gaps. Since the neural network performance should be invariant to image transformations, we look to this fact to identify uncertain pseudo labels. Indeed, we argue that transformation invariant detections can provide more reasonable approximations of ground truth. Accordingly, we propose a semi-supervised learning strategy for domain adaptation termed transformation-invariant self-training (TI-ST). the proposed method assesses pixel-wise pseudo-labels' reliability and filters out unreliable detections during self-training. We perform comprehensive evaluations for domain adaptation using three different modalities of medical images, two different network architectures, and several alternative state-of-the-art domain adaptation methods. Experimental results confirm the superiority of our proposed method in mitigating the lack of target domain annotation and boosting segmentation performance in the target domain.

关键词： Semi-Supervised Learning Domain Adaptation Semantic Segmentation Self Training Cataract Surgery MRI OCT

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Self-aware and Cross-Sample Prototypical Learning for Semi-supervised medical image Segmentation 26th

Self-aware and Cross-Sample Prototypical Learning for Semi-s...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Zhang, Zhenxi Ran, Ran Tian, Chunna Zhou, Heng Li, Xin Yang, Fan Jiao, Zhicheng Xidian Univ 2 South Taibai Rd Xian Shanxi Peoples R China Xi An Jiao Tong Univ Ctr Canc Affiliated Hosp 1 Xian Peoples R China AIQ Abu Dhabi U Arab Emirates Brown Univ Warren Alpert Med Sch Dept Diagnost Imaging Providence RI 02912 USA

ISBN: (纸本)9783031438943;9783031438950

Consistency learning plays a crucial role in semi-supervised medical image segmentation as it enables the effective utilization of limited annotated data while leveraging the abundance of unannotated data. the effectiveness and efficiency of consistency learning are challenged by prediction diversity and training stability, which are often overlooked by existing studies. Meanwhile, the limited quantity of labeled data for training often proves inadequate for formulating intra-class compactness and inter-class discrepancy of pseudo labels. To address these issues, we propose a self-aware and cross-sample prototypical learning method (SCP-Net) to enhance the diversity of prediction in consistency learning by utilizing a broader range of semantic information derived from multiple inputs. Furthermore, we introduce a self-aware consistency learningmethodwhich exploits unlabeled data to improve the compactness of pseudo labels within each class. Moreover, a dual loss re-weightingmethod is integrated into the cross-sample prototypical consistency learningmethod to improve the reliability and stability of ourmodel. Extensive experiments onACDCdataset and PROMISE12 dataset validate that SCP-Net outperforms other state-of-the-art semi-supervised segmentation methods and achieves significant performance gains compared to the limited supervised training. Code is available at https://***/Medsemiseg/SCP- Net.

关键词： Prototypical learning Consistency learning Semi-supervised segmentation

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Weakly Supervised medical image Segmentation via Superpixel-Guided Scribble Walking and Class-Wise Contrastive Regularization 26th

Weakly Supervised Medical Image Segmentation via Superpixel-...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Zhou, Meng Xu, Zhe Zhou, Kang Tong, Raymond Kai-yu Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9783031438943;9783031438950

Deep learning-based segmentation typically requires a large amount of data with dense manual delineation, which is both time-consuming and expensive to obtain for medical images. Consequently, weakly supervised learning, which attempts to utilize sparse annotations such as scribbles for effective training, has garnered considerable attention. However, such scribble-supervision inherently lacks sufficient structural information, leading to two critical challenges: (i) while achieving good performance in overall overlap metrics such as Dice score, the existing methods struggle to perform satisfactory local prediction because no desired structural priors are accessible during training;(ii) the class feature distributions are inevitably less-compact due to sparse and extremely incomplete supervision, leading to poor generalizability. To address these, in this paper, we propose the SC-Net, a new scribble-supervised approach that combines Superpixel-guided scribble walking with Class-wise contrastive regularization. Specifically, the framework is built upon the recent dual-decoder backbone design, where predictions from two slightly different decoders are randomly mixed to provide auxiliary pseudo-label supervision. Besides the sparse and pseudo supervision, the scribbles walk towards unlabeled pixels guided by superpixel connectivity and image content to offer as much dense supervision as possible. then, the class-wise contrastive regularization disconnects the feature manifolds of different classes to encourage the compactness of class feature distributions. We evaluate our approach on the public cardiac dataset ACDC and demonstrate the superiority of our method compared to recent scribble-supervised and semi-supervised learning methods with similar labeling efforts.

关键词： Weakly-supervised Learning Segmentation Superpixel

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