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Cascaded Diffusion Models for 2D and 3D Microscopy Image Synthesis to Enhance Cell Segmentation 22

Cascaded Diffusion Models for 2D and 3D Microscopy Image Syn...

引用

22nd IEEE International Symposium on Biomedical imaging, ISBI 2025

作者： Yilmaz, Ruveyda Keven, Kaan Wu, Yuli Stegmaier, Johannes Institute of Imaging and Computer Vision Rwth Aachen University Germany

ISBN: (纸本)9798331520526

Automated cell segmentation in microscopy images is essential for biomedical research, yet conventional methods are labor-intensive and prone to error. While deep learning-based approaches have proven effective, they often require large annotated datasets, which are scarce due to the challenges of manual annotation. To overcome this, we propose a novel framework for synthesizing densely annotated 2D and 3D cell microscopy images using cascaded diffusion models. Our method synthesizes 2D and 3D cell masks from sparse 2D annotations using multi-level diffusion models and NeuS, a 3D surface reconstruction approach. Following that, a pretrained 2D Stable Diffusion model is finetuned to generate realistic cell textures and the final outputs are combined to form cell populations. We show that training a segmentation model with a combination of our synthetic data and real data improves cell segmentation performance by up to 9% across multiple datasets. Additionally, the FID scores indicate that the synthetic data closely resembles real data. The code for our proposed approach will be available at https://***/ruveydayilmaz0/cascaded_diffusion. © 2025 IEEE.

关键词： Image segmentation

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Weakly-supervised Temporal Segmentation of Cell-cycle Stages with Center-cell Focus using Recurrent Neural Networks

Weakly-supervised Temporal Segmentation of Cell-cycle Stages...

引用

Bildverarbeitung für die Medizin Workshop, BVM 2023

作者： Jose, Abin Roy, Rĳo Stegmaier, Johannes Institute of Imaging and Computer Vision RWTH Aachen University Germany

ISBN: (纸本)9783658416560

Training deep-learning models for biomedical images has always been a problem due to the lack of annotated data. Here we propose using a model and a training approach for the weakly-supervised temporal classification of cell-cycle stages during mitosis. Instead of using annotated data, by using an ordered set of classes called transcript, our proposed approach classifies the cell-cycle stages of cell video sequences. The network design helps to propagate information in time using Recurrent Neural Network and helps to focus the features on the center-cell. The algorithm is evaluated on four datasets from Moreno-Andrés et al. [1] and has a performance close to the supervised approaches, which is impressive, considering that annotated data is not used in training. © 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature.

关键词： Recurrent neural networks

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Weighted Edge Sharpening Filtering for Upscaled Content in Adaptive Resolution Coding

Weighted Edge Sharpening Filtering for Upscaled Content in A...

引用

2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023

作者： Clasen, Tim Wien, Mathias RWTH Aachen University Institute of Imaging and Computer Vision Aachen Germany

ISBN: (纸本)9798350359855

Reference Picture Resampling (RPR) is an essential tool for quickly adapting to varying network conditions. It enables a resolution change without the introduction of an intra random access point (IRAP). This feature is particularly crucial in real-time transmission scenarios and has proven effective for compressing high-resolution pictures. However, a significant challenge of Reference Picture Resampling is the loss of high-frequency information in the downscaling operation. This results in blurred pictures after upscaling, which significantly affects the viewer experience. To address this issue, we propose an additional enhancement step after upscaling. The proposed enhancement method involves a locally weighted adaptive filter specifically designed for edge sharpening. Through the application of local weighting, we can avoid the common problems associated with linear high-pass filters. Picture-wise content adaptivity helps in handling different scene characteristics. The proposed method is implemented into the enhanced compression model 8.0 (ECM) and achieves performance gains for the joint video experts team (JVET) common testing conditions for reference picture resampling (RPR), with a Bjontegaard delta-rate (BD-rate) reduction of -7.27%, -0.57%, and -0.32% for the Y, Cb, and Cr channels, respectively. © 2023 IEEE.

关键词： Adaptive filtering

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A Template Matching Approach for Reference Picture Padding in Video Coding 48

A Template Matching Approach for Reference Picture Padding i...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Horst, Nicolas Das, Priyanka Wien, Mathias RWTH Aachen University Institute of Imaging and Computer Vision Aachen Germany

ISBN: (纸本)9781728163277

Reference picture padding is needed in areas close to the picture boundary. It lifts the restriction of motion vectors not to point over the boundary when using inter prediction. Especially, when independently decodable subpictures are used e.g. in viewport-Adaptive streaming, many boundaries occur, where padding is necessary. In such cases, the prediction quality of reference (sub)picture padding has an increased impact on the coding performance. Template matching has shown to work well for texture prediction in video coding. The paper shows that it also improves coding performance when applied in the context of reference picture padding. Experimental results on a set of test sequences demonstrate consistent coding gains and an average Bjontegaard delta rate reduction of-0.38% and-0.48% for two sets of sequences with frequent utilization of reference picture padding. A drawback of template matching is its computational complexity. A preliminary investigation shows that the complexity can be reduced by a factor of 330 while maintaining about 60% of the Bjontegaard delta rate savings. © 2023 IEEE.

关键词： Template matching

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No Free Lunch in Annotation Either: An Objective Evaluation of Foundation Models for Streamlining Annotation in Animal Tracking 22

No Free Lunch in Annotation Either: An Objective Evaluation ...

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22nd IEEE International Symposium on Biomedical imaging, ISBI 2025

作者： Mededovic, Emil Laurentius, Valdy Wu, Yuli Kopaczka, Marcin Chen, Zhu Schulz, Mareike Tolba, René Stegmaier, Johannes Institute of Imaging and Computer Vision Rwth Aachen University Aachen Germany Institute for Laboratory Animal Science Rwth Aachen University Germany

ISBN: (纸本)9798331520526

We analyze the capabilities of foundation models addressing the tedious task of generating annotations for animal tracking. Annotating a large amount of data is vital and can be a make-or-break factor for the robustness of a tracking model. Ro-bustness is particularly crucial in animal tracking, as accurate tracking over long time horizons is essential for capturing the behavior of animals. However, generating additional annotations using foundation models can be counterproductive, as the quality of the annotations is just as important. Poorly annotated data can introduce noise and inaccuracies, ultimately compromising the performance and accuracy of the trained model. Over-reliance on automated annotations without ensuring precision can lead to diminished results, making careful oversight and quality control essential in the annotation process. Ultimately, we demonstrate that a thoughtful combination of automated annotations and manually annotated data is a valuable strategy, yielding an IDF1 score of 80.8 against blind usage of SAM2 video with an IDF1 score of 65.6. Our implementation for the annotation tool is available at: https://***/medem23/SAM-QA. © 2025 IEEE.

关键词： Model predictive control

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Comparative Analysis of Unsupervised and Supervised Autoencoders for Nuclei Classification in Clear Cell Renal Cell Carcinoma Images 22

Comparative Analysis of Unsupervised and Supervised Autoenco...

引用

22nd IEEE International Symposium on Biomedical imaging, ISBI 2025

作者： Javadian, Fatemeh Aminparast, Zahra Stegmaier, Johannes Jose, Abin Institute of Imaging and Computer Vision Rwth Aachen University Aachen Germany Institute of Medical Sciences Kermanshah University Medical School Kermanshah Iran

ISBN: (纸本)9798331520526

This study explores the application of supervised and unsupervised autoencoders (AEs) to automate nuclei classification in clear cell renal cell carcinoma (ccRCC) images, a critical diagnostic task that traditionally relies on subjective visual grading by pathologists. We evaluate various AE architectures, including standard AEs, contractive AEs (CAEs), and discriminative AEs (DAEs), as well as a classifier-based discriminative AE (CDAE), optimized using the hyperparameter tuning tool Optuna. Bhattacharyya distance is selected from several metrics to assess class separability within the latent space for different architectures, revealing challenges in distinguishing adjacent grades using fully unsupervised models. CDAE, integrating a supervised classifier branch, demonstrated superior performance in both latent space separation and classification accuracy. Given that CDAE-CNN achieved notable improvements in classification metrics, affirming the importance of supervised learning for enhancing class-specific feature extraction in ccRCC nuclei, F1 score was incorporated into the hyperparameter tuning process to achieve the best-fitting model. The results demonstrate distinct improvements in identifying more aggressive ccRCC grades by leveraging the classification capability of AE through latent space clustering followed by supervised fine-grained classification so that our model outperfotms state of the art, CHR-Network, in all evaluated metrics. Our findings suggest that not only does the inclusion of a classifier branch in AEs provide a promising approach for improving grading automation in ccRCC pathology compared to unsupervised clustering or supervised learning alone, but the use of neural architecture search combined with contrastive learning techniques to structure embeddings in the latent space of the AE architecture also leads to enhanced performance, particularly in identifying more aggressive grades, which are more influential in tumor grading. This advance

关键词： Contrastive Learning

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Visual Fixation-Based Retinal Prosthetic Simulation 22

Visual Fixation-Based Retinal Prosthetic Simulation

引用

22nd IEEE International Symposium on Biomedical imaging, ISBI 2025

作者： Wu, Yuli Nguyen, Do Dinh Tan Konermann, Henning Yilmaz, Ruveyda Walter, Peter Stegmaier, Johannes Institute of Imaging and Computer Vision Rwth Aachen University Aachen Germany Rwth Aachen University Department of Ophthalmology Aachen Germany

ISBN: (纸本)9798331520526

This study proposes a retinal prosthetic simulation framework driven by visual fixations, inspired by the saccade mechanism, and assesses performance improvements through end-to-end optimization in a classification task. Salient patches are predicted from input images using the self-attention map of a vision transformer to mimic visual fixations. These patches are then encoded by a trainable U-Net and simulated using the pulse2percept framework to predict visual percepts. By incorporating a learnable encoder, we aim to optimize the visual information transmitted to the retinal implant, addressing both the limited resolution of the electrode array and the distortion between the input stimuli and resulting phosphenes. The predicted percepts are evaluated using the self-supervised DINOv2 foundation model, with an optional learnable linear layer for classification accuracy. On a subset of the ImageNet validation set, the fixation-based framework achieves a classification accuracy of 87.72%, using computational parameters based on a real subject's physiological data, significantly outperforming the downsampling-based accuracy of 40.59% and approaching the healthy upper bound of 92.76%. Our approach shows promising potential for producing more semantically understandable percepts with the limited resolution available in retinal prosthetics. © 2025 IEEE.

关键词： Physiological models

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Evaluation of OCT Image Synthesis for Choroidal and Retinal Layer Segmentation Using Denoising Diffusion Probabilistic Models

Evaluation of OCT Image Synthesis for Choroidal and Retinal ...

引用

18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Yamauchi, Yudai Wu, Yuli Okada, Eiji Keio University Department of Electronics and Electrical Engineering Japan Institute of Imaging and Computer Vision RWTH Aachen University Germany

Machine learning can automatically conduct the layer segmentation task of retinal optical coherence tomog raphy (OCT) image, but annotated data is required to train these models. Synthetic retinal OCT images are generated using denoising diffusion probabilistic models (DDPMs), which can be used to train segmentation models effectively and automatically create annotated data. However, the extent to which these synthetic im ages contribute to segmentation accuracy compared to real data has not been investigated. In this study, we synthesized retinal OCT images from sketch images using DDPMs, trained a segmentation model using syn thetic and real images, and evaluated how the use of synthetic images influenced the accuracy of choroidal and retinal layer segmentation compared to results using only real images. Through a comparison of the Dice score, we confirmed that training with both synthetic and real OCT images led to higher Dice scores than train ing with only real OCT images. These findings suggest that using synthetic images can enhance segmentation accuracy, offering a promising approach to improving model performance in situations with limited annotated real data. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Image segmentation

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LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction 39

LogicAD: Explainable Anomaly Detection via VLM-based Text Fe...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jin, Er Feng, Qihui Mou, Yongli Lakemeyer, Gerhard Decker, Stefan Simons, Oliver Stegmaier, Johannes Institute of Imaging and Computer Vision RWTH Aachen University Aachen Germany Department of Computer Science RWTH Aachen University Aachen Germany Fraunhofer Institute for Applied Information Technology FIT Sankt Augustin Germany

ISBN: (纸本)157735897X

Logical image understanding involves interpreting and reasoning about the relationships and consistency within an image’s visual content. This capability is essential in applications such as industrial inspection, where logical anomaly detection (AD) is critical for maintaining high-quality standards and minimizing costly recalls. Previous research in AD has relied on prior knowledge for designing algorithms, which often requires extensive manual annotation effort, significant computing power, and large amounts of data for training. Autoregressive, multimodal vision Language Models (AVLMs) offer a promising alternative due to their exceptional performance in visual reasoning across various domains. Despite this, their application in logical AD remains unexplored. In this work, we investigate using AVLMs for logical AD and demonstrate that they are well-suited to the task. Combining AVLMs with format embedding and a logic reasoner, we achieve state-of-the-art (SOTA) AD performance on public benchmarks, MVTec LOCO AD, with an AUROC of 86.0% and an F1-max of 83.7% along with explanations of the anomalies. This significantly outperforms the existing SOTA method by 18.1% in AUROC and 4.6% in F1-max score. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Anomaly detection

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Enhancing Lidar-Based Object Detection in Adverse Weather Using Offset Sequences in Time

Enhancing Lidar-Based Object Detection in Adverse Weather Us...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Van Kempen, Raphael Rehbronn, Tim Jose, Abin Stegmaier, Johannes Lampe, Bastian Woopen, Timo Eckstein, Lutz Institute for Automotive Engineering Rwth Aachen University Aachen Germany Institute of Imaging and Computer Vision Rwth Aachen University Aachen Germany

ISBN: (纸本)9798350327816

Automated vehicles require an accurate perception of their surroundings for safe and efficient driving. Lidar-based object detection is a widely used method for environment perception, but its performance is significantly affected by adverse weather conditions such as rain and fog. In this work, we investi-gate various strategies for enhancing the robustness of lidar-based object detection by processing sequential data samples generated by lidar sensors. Our approaches leverage temporal information to improve a lidar object detection model, without the need for additional filtering or preprocessing steps. We compare 10 different neural network architectures that process point cloud sequences including a novel augmentation strategy introducing a temporal offset between frames of a sequence during training and evaluate the effectiveness of all strategies on lidar point clouds under adverse weather conditions through experiments. Our research provides a comprehensive study of effective methods for mitigating the effects of adverse weather on the reliability of lidar-based object detection using sequential data that are evaluated using public datasets such as nuScenes, Dense, and the Canadian Adverse Driving Conditions Dataset. Our findings demonstrate that our novel method, involving temporal offset augmentation through randomized frame skipping in sequences, enhances object detection accuracy compared to both the baseline model (Pillar-based Object Detection) and no augmentation. © 2023 IEEE.

关键词： Adverse Weather Automated Driving Deep Learning Lidar Perception

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