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Surgical Scene Segmentation Using Semantic image Synthesis with a Virtual Surgery Environment 25th

Surgical Scene Segmentation Using Semantic Image Synthesis w...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Yoon, Jihun Hong, SeulGi Hong, Seungbum Lee, Jiwon Shin, Soyeon Park, Bokyung Sung, Nakjun Yu, Hayeong Kim, Sungjae Park, SungHyun Hyung, Woo Jin Choi, Min-Kook Hutom Seoul South Korea Yonsei Univ Dept Surg Coll Med Seoul South Korea

ISBN: (纸本)9783031164491;9783031164484

the previous image synthesis research for surgical vision had limited results for real-world applications with simple simulators, including only a few organs and surgical tools and outdated segmentation models to evaluate the quality of the image. Furthermore, none of the research released complete datasets to the public enabling the open research. therefore, we release a new dataset to encourage further study and provide novel methods with extensive experiments for surgical scene segmentation using semantic image synthesis with a more complex virtual surgery environment. First, we created three cross-validation sets of real image data considering demographic and clinical information from 40 cases of real surgical videos of gastrectomy with the da Vinci Surgical System (dVSS). Second, we created a virtual surgery environment in the Unity engine with five organs from real patient CT data and 22 the da Vinci surgical instruments from actual measurements. third, We converted this environment photo-realistically with representative semantic image synthesis models, SEAN and SPADE. Lastly, we evaluated it with various state-of-the-art instance and semantic segmentation models. We succeeded in highly improving our segmentation models with the help of synthetic training data. More methods, statistics, and visualizations on https://***/.

关键词： Surgical instrument localization Class imbalance Domain randomization Synthetic data Semantic image snythesis

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GSMorph: Gradient Surgery for Cine-MRI Cardiac Deformable Registration 26th

GSMorph: Gradient Surgery for Cine-MRI Cardiac Deformable Re...

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

作者： Dou, Haoran Bi, Ning Han, Luyi Huang, Yuhao Mann, Ritse Yang, Xin Ni, Dong Ravikumar, Nishant Frangi, Alejandro F. Huang, Yunzhi Univ Leeds Ctr Computat Imaging & Simulat Technol Biomed CIS Leeds W Yorkshire England Radboud Univ Nijmegen Dept Radiol & Nucl Med Med Ctr Nijmegen Netherlands Netherlands Canc Inst Dept Radiol Amsterdam Netherlands Shenzhen Univ Sch Biomed Engn Hlth Sci Ctr Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China Shenzhen Univ Med Ultrasound Image Comp MUSIC Lab Shenzhen Peoples R China Shenzhen Univ Marshall Lab Biomed Engn Shenzhen Peoples R China Shenzhen RayShape Med Technol Co Ltd Shenzhen Peoples R China Univ Manchester Sch Comp Sci & Hlth Sci Div Informat Imaging & Data Sci Manchester Lancs England Katholieke Univ Leuven Med Imaging Res Ctr MIRC Elect Engn Dept Leuven Belgium Katholieke Univ Leuven Med Imaging Res Ctr MIRC Cardiovasc Sci Dept Leuven Belgium Alan Turing Inst London England Nanjing Univ Informat Sci & Technol Inst Med Sch Artificial Intelligence Nanjing Peoples R China

ISBN: (纸本)9783031439988;9783031439995

Deep learning-based deformable registration methods have been widely investigated in diverse medical applications. Learning-based deformable registration relies on weighted objective functions trading off registration accuracy and smoothness of the deformation field. therefore, they inevitably require tuning the hyperparameter for optimal registration performance. Tuning the hyperparameters is highly computationally expensive and introduces undesired dependencies on domain knowledge. In this study, we construct a registration model based on the gradient surgery mechanism, named GSMorph, to achieve a hyperparameter-free balance on multiple losses. In GSMorph, we reformulate the optimization procedure by projecting the gradient of similarity loss orthogonally to the plane associated with the smoothness constraint, rather than additionally introducing a hyperparameter to balance these two competing terms. Furthermore, our method is model-agnostic and can be merged into any deep registration network without introducing extra parameters or slowing down inference. In this study, We compared our method with state-of-the-art (SOTA) deformable registration approaches over two publicly available cardiac MRI datasets. GSMorph proves superior to five SOTA learning-based registration models and two conventional registration techniques, SyN and Demons, on both registration accuracy and smoothness.

关键词： medical image registration Gradient surgery Regularization

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Implicit Neural Representations for Generative Modeling of Living Cell Shapes 25th

Implicit Neural Representations for Generative Modeling of L...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Wiesner, David Suk, Julian Dummer, Sven Svoboda, David Wolterink, Jelmer M. Masaryk Univ Ctr Biomed Image Anal Brno Czech Republic Univ Twente Dept Appl Math Enschede Netherlands Univ Twente Tech Med Ctr Enschede Netherlands

ISBN: (纸本)9783031164408;9783031164392

Methods allowing the synthesis of realistic cell shapes could help generate training data sets to improve cell tracking and segmentation in biomedical images. Deep generative models for cell shape synthesis require a light-weight and flexible representation of the cell shape. However, commonly used voxel-based representations are unsuitable for high-resolution shape synthesis, and polygon meshes have limitations when modeling topology changes such as cell growth or mitosis. In this work, we propose to use level sets of signed distance functions (SDFs) to represent cell shapes. We optimize a neural network as an implicit neural representation of the SDF value at any point in a 3D+time domain. the model is conditioned on a latent code, thus allowing the synthesis of new and unseen shape sequences. We validate our approach quantitatively and qualitatively on C. elegans cells that grow and divide, and lung cancer cells with growing complex filopodial protrusions. Our results show that shape descriptors of synthetic cells resemble those of real cells, and that our model is able to generate topologically plausible sequences of complex cell shapes in 3D+time.

关键词： Cell shape modeling Neural networks Implicit neural representations Signed distance function Generative model Interpolation

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Hand Hygiene Quality Assessment Using image-to-image Translation 25th

Hand Hygiene Quality Assessment Using Image-to-Image Transla...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Wang, Chaofan Yang, Kangning Jiang, Weiwei Wei, Jing Sarsenbayeva, Zhanna Goncalves, Jorge Kostakos, Vassilis Univ Melbourne Sch Comp & Informat Syst Melbourne Vic Australia

ISBN: (纸本)9783031164491;9783031164484

Hand hygiene can reduce the transmission of pathogens and prevent healthcare-associated infections. Ultraviolet (UV) test is an effective tool for evaluating and visualizing hand hygiene quality during medical training. However, due to various hand shapes, sizes, and positions, systematic documentation of the UV test results to summarize frequently untreated areas and validate hand hygiene technique effectiveness is challenging. Previous studies often summarize errors within predefined hand regions, but this only provides low-resolution estimations of hand hygiene quality. Alternatively, previous studies manually translate errors to hand templates, but this lacks standardized observational practices. In this paper, we propose a novel automatic image-to-image translation framework to evaluate hand hygiene quality and document the results in a standardized manner. the framework consists of two models, including an Attention U-Net model to segment hands from the background and simultaneously classify skin surfaces covered with hand disinfectants, and a U-Net-based generator to translate the segmented hands to hand templates. Moreover, due to the lack of publicly available datasets, we conducted a lab study to collect 1218 valid UV test images containing different skin coverage with hand disinfectants. the proposed framework was then evaluated on the collected dataset through five-fold cross-validation. Experimental results show that the proposed framework can accurately assess hand hygiene quality and document UV test results in a standardized manner. the benefit of our work is that it enables systematic documentation of hand hygiene practices, which in turn enables clearer communication and comparisons.

关键词： Hand hygiene Handrub Six-step hand hygiene technique Healthcare-associated infections Nosocomial infections

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Deep Motion Network for Freehand 3D Ultrasound Reconstruction 25th

Deep Motion Network for Freehand 3D Ultrasound Reconstructio...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Luo, Mingyuan Yang, Xin Wang, Hongzhang Du, Liwei Ni, Dong Shenzhen Univ Hlth Sci Ctr Sch Biomed Engn Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China Shenzhen Univ Med Ultrasound Image Comp MUSIC Lab Shenzhen Peoples R China Shenzhen Univ Marshall Lab Biomed Engn Shenzhen Peoples R China

ISBN: (纸本)9783031164408;9783031164392

Freehand 3D ultrasound (US) has important clinical value due to its low cost and unrestricted field of view. Recently deep learning algorithms have removed its dependence on bulky and expensive external positioning devices. However, improving reconstruction accuracy is still hampered by difficult elevational displacement estimation and large cumulative drift. In this context, we propose a novel deep motion network (MoNet) that integrates images and a lightweight sensor known as the inertial measurement unit (IMU) from a velocity perspective to alleviate the obstacles mentioned above. Our contribution is two-fold. First, we introduce IMU acceleration for the first time to estimate devotional displacements outside the plane. We propose a temporal and multi-branch structure to mine the valuable information of low signalto-noise ratio (SNR) acceleration. Second, we propose a multi-modal online self-supervised strategy that leverages IMU information as weak labels for adaptive optimization to reduce drift errors and further ameliorate the impacts of acceleration noise. Experiments show that our proposed method achieves the superior reconstruction performance, exceeding state-of-the-art methods across the board.

关键词： Inertial measurement unit Online learning Freehand 3D ultrasound

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Noise2SR: Learning to Denoise from Super-Resolved Single Noisy Fluorescence image 25th

Noise2SR: Learning to Denoise from Super-Resolved Single Noi...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Tian, Xuanyu Wu, Qing Wei, Hongjiang Zhang, Yuyao ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Biomed Engn Shanghai Peoples R China ShanghaiTech Univ Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (纸本)9783031164460;9783031164453

Fluorescence microscopy is a key driver to promote discoveries of biomedical research. However, with the limitation of microscope hardware and characteristics of the observed samples, the fluorescence microscopy images are susceptible to noise. Recently, a few self-supervised deep learning (DL) denoising methods have been proposed. However, the training efficiency and denoising performance of existing methods are relatively low in real scene noise removal. To address this issue, this paper proposed self-supervised image denoising method Noise2SR (N2SR) to train a simple and effective image denoising model based on single noisy observation. Our Noise2SR denoising model is designed for training with paired noisy images of different dimensions. Benefiting from this training strategy, Noise2SR is more efficiently self-supervised and able to restore more image details from a single noisy observation. Experimental results of simulated noise and real microscopy noise removal show that Noise2SR outperforms two blind-spot based self-supervised deep learning image denoising methods. We envision that Noise2SR has the potential to improve more other kind of scientific imaging quality.

关键词： image denoising Self-supervised learning Fluorescence microscopy image

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A Transformer-Based Iterative Reconstruction Model for Sparse-View CT Reconstruction 25th

A Transformer-Based Iterative Reconstruction Model for Spars...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Xia, Wenjun Yang, Ziyuan Zhou, Qizheng Lu, Zexin Wang, Zhongxian Zhang, Yi Sichuan Univ Chengdu 610065 Peoples R China SUNY Stony Brook New York NY 11794 USA

ISBN: (纸本)9783031164460;9783031164453

Sparse-view computed tomography (CT) is one of the primary means to reduce the radiation risk. But the reconstruction of sparse-view CT will be contaminated by severe artifacts. By carefully designing the regularization terms, the iterative reconstruction (IR) algorithm can achieve promising results. With the introduction of deep learning techniques, learned regularization terms with convolution neural network (CNN) attracts much attention and can further improve the performance. In this paper, we propose a learned local-nonlocal regularization-based model called RegFormer to reconstruct CT images. Specifically, we unroll the iterative scheme into a neural network and replace handcrafted regularization terms with learnable kernels. the convolution layers are used to learn local regularization with excellent denoising performance. Simultaneously, transformer encoders and decoders incorporate the learned nonlocal prior into the model, preserving the structures and details. To improve the ability to extract deep features during iteration, we introduce an iteration transmission (IT) module, which can further promote the efficiency of each iteration. the experimental results show that our proposed RegFormer achieves competitive performance in artifact reduction and detail preservation compared to some state-of-the-art sparse-view CT reconstruction methods.

关键词： Computed tomography image reconstruction Deep learning Nonlocal regularization Transformer

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Kernel Attention Transformer (KAT) for Histopathology Whole Slide image Classification 25th

Kernel Attention Transformer (KAT) for Histopathology Whole ...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Zheng, Yushan Li, Jun Shi, Jun Xie, Fengying Jiang, Zhiguo Beihang Univ Beijing Adv Innovat Ctr Biomed Engn Sch Engn Med Beijing 100191 Peoples R China Beihang Univ Sch Astronaut Image Proc Ctr Beijing 102206 Peoples R China Hefei Univ Technol Sch Software Hefei 230601 Peoples R China

ISBN: (纸本)9783031164347;9783031164330

Transformer has been widely used in histopathology whole slide image (WSI) classification for the purpose of tumor grading, prognosis analysis, etc. However, the design of token-wise self-attention and positional embedding strategy in the common Transformer limits the effectiveness and efficiency in the application to gigapixel histopathology images. In this paper, we propose a kernel attention Transformer (KAT) for histopathology WSI classification. the information transmission of the tokens is achieved by cross-attention between the tokens and a set of kernels related to a set of positional anchors on the WSI. Compared to the common Transformer structure, the proposed KAT can better describe the hierarchical context information of the local regions of the WSI and meanwhile maintains a lower computational complexity. the proposed method was evaluated on a gastric dataset with 2040 WSIs and an endometrial dataset with 2560 WSIs, and was compared with 6 state-of-the-art methods. the experimental results have demonstrated the proposed KAT is effective and efficient in the task of histopathology WSI classification and is superior to the state-of-the-art methods. the code is available at https://***/zhengyushan/kat.

关键词： WSI classification Transformer Cross-attention

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Fast FF-to-FFPE Whole Slide image Translation via Laplacian Pyramid and Contrastive Learning 25th

Fast FF-to-FFPE Whole Slide Image Translation via Laplacian ...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Fan, Lei Sowmya, Arcot Meijering, Erik Song, Yang Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia

ISBN: (纸本)9783031164347;9783031164330

Formalin-Fixed Paraffin-Embedded (FFPE) and Fresh Frozen (FF) are two major types of histopathological Whole Slide images (WSIs). FFPE provides high-quality images, however the acquisition process usually takes 12 to 48 h, while FF with relatively low-quality images takes less than 15 min to acquire. In this work, we focus on the task of translating FF to FFPE style (FF2FFPE), to synthesize FFPE-style images from FF images. However, WSIs with giga-pixels impose heavy constraints on computation and time resources. To address these issues, we propose the fastFF2FFPE for translating FF into FFPE-style efficiently. Specifically, we decompose FF images into low- and high-frequency components based on the Laplacian Pyramid, wherein the low-frequency component at low resolution is transformed into FFPEstyle with low computational cost, and the high-frequency component is used for providing details. We further employ contrastive learning to encourage similarities between original and output patches. We conduct FF2FFPE translation experiments on the Cancer Genome Atlas (TCGA) Glioblastoma Multiforme (GBM) and Lung Squamous Cell Carcinoma (LUSC) datasets, and verify the efficacy of our model on Microsatellite Instability prediction in gastrointestinal cancer. the code and models are released at https://***/hellodfan/fastFF2FFPE.

关键词： image translation Histopathology Laplacian pyramid Whole Slide images Contrastive learning

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On the Dataset Quality Control for image Registration Evaluation 25th

On the Dataset Quality Control for Image Registration Evalua...

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Luo, Jie Ma, Guangshen Haouchine, Nazim Xu, Zhe Wang, Yixin Kapur, Tina Ning, Lipeng Wells, William M., III Frisken, Sarah Harvard Med Sch Brigham & Womens Hosp Boston MA USA Duke Univ Dept Mech Engn & Mat Sci Durham NC USA Chinese Univ Hong Kong Dept Biomed Engn Hong Kong Peoples R China Stanford Univ Dept Bioengn Stanford CA USA

ISBN: (纸本)9783031164460;9783031164453

Current registration evaluations typically compute target registration error using manually annotated datasets. As a result, the quality of landmark annotations is crucial for unbiased comparisons because registration algorithms are trained and tested using these landmarks. Even though some data providers claim to have mitigated the inter-observer variability by having multiple raters, quality control such as a third-party screening can still be reassuring for intended users. Examining the landmark quality for neurosurgical datasets (RESECT and BITE) poses specific challenges. In this study, we applied the variogram, which is a tool extensively used in geostatistics, to convert 3D landmark distributions into an intuitive 2D representation. this allowed us to identify potential problematic cases efficiently so that they could be examined by experienced radiologists. In both the RESECT and BITE datasets, we identified and confirmed a small number of landmarks with potential localization errors and found that, in some cases, the landmark distribution was not ideal for an unbiased assessment of non-rigid registration errors. Under discussion, we provide some constructive suggestions for improving the utility of publicly available annotated data.

关键词： image registration Public datasets Quality control

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