Image and Video Processing (eess.IV)

Abstract: Directed energy applications require the estimation of digital-holographic (DH) phase errors due to atmospheric turbulence in order to accurately focus the outgoing beam. These phase error estimates must be computed with very low latency to keep pace with changing atmospheric parameters, which requires that phase errors be estimated in a single shot of DH data. The digital holography model-based iterative reconstruction (DH-MBIR) algorithm is capable of accurately estimating phase errors in a single shot using the expectation maximization (EM) algorithm. However, existing implementations of DH-MBIR require hundreds of iterations, which is not practical for real-time applications. In this paper, we present the Dynamic DH-MBIR (DDH-MBIR) algorithm for estimating isoplanatic phase errors from streaming single-shot data with extremely low latency. The Dynamic DH-MBIR algorithm reduces the computation and latency by orders of magnitude relative to conventional DH-MBIR, making real-time throughput and latency feasible in applications. Using simulated data that models frozen flow of atmospheric turbulence, we show that our algorithm can achieve a consistently high Strehl ratio with realistic simulation parameters using only 1 iteration per timestep.

Abstract: The paper proposes a Federated Content-Based Medical Image Retrieval (FedCBMIR) platform that utilizes Federated Learning (FL) to address the challenges of acquiring a diverse medical data set for training CBMIR models. CBMIR assists pathologists in diagnosing breast cancer more rapidly by identifying similar medical images and relevant patches in prior cases compared to traditional cancer detection methods. However, CBMIR in histopathology necessitates a pool of Whole Slide Images (WSIs) to train to extract an optimal embedding vector that leverages search engine performance, which may not be available in all centers. The strict regulations surrounding data sharing in medical data sets also hinder research and model development, making it difficult to collect a rich data set. The proposed FedCBMIR distributes the model to collaborative centers for training without sharing the data set, resulting in shorter training times than local training. FedCBMIR was evaluated in two experiments with three scenarios on BreaKHis and Camelyon17 (CAM17). The study shows that the FedCBMIR method increases the F1-Score (F1S) of each client to 98%, 96%, 94%, and 97% in the BreaKHis experiment with a generalized model of four magnifications and does so in 6.30 hours less time than total local training. FedCBMIR also achieves 98% accuracy with CAM17 in 2.49 hours less training time than local training, demonstrating that our FedCBMIR is both fast and accurate for both pathologists and engineers. In addition, our FedCBMIR provides similar images with higher magnification for non-developed countries where participate in the worldwide FedCBMIR with developed countries to facilitate mitosis measuring in breast cancer diagnosis. We evaluate this scenario by scattering BreaKHis into four centers with different magnifications.

Abstract: In recent years, videos and images in 720p (HD), 1080p (FHD) and 4K (UHD) resolution have become more popular for display devices such as TVs, mobile phones and VR. However, these high resolution images cannot achieve the expected visual effect due to the limitation of the internet bandwidth, and bring a great challenge for super-resolution networks to achieve real-time performance. Following this challenge, we explore multiple efficient network designs, such as pixel-unshuffle, repeat upscaling, and local skip connection removal, and propose a fast and lightweight super-resolution network. Furthermore, by analyzing the applications of the idea of divide-and-conquer in super-resolution, we propose assembled convolutions which can adapt convolution kernels according to the input features. Experiments suggest that our method outperforms all the state-of-the-art efficient super-resolution models, and achieves optimal results in terms of runtime and quality. In addition, our method also wins the first place in NTIRE 2023 Real-Time Super-Resolution - Track 1 ($\times$2). The code will be available at https://gitee.com/mindspore/models/tree/master/research/cv/AsConvSR

Abstract: The human thalamus is a highly connected subcortical grey-matter structure within the brain. It comprises dozens of nuclei with different function and connectivity, which are affected differently by disease. For this reason, there is growing interest in studying the thalamic nuclei in vivo with MRI. Tools are available to segment the thalamus from 1 mm T1 scans, but the contrast of the lateral and internal boundaries is too faint to produce reliable segmentations. Some tools have attempted to incorporate information from diffusion MRI in the segmentation to refine these boundaries, but do not generalise well across diffusion MRI acquisitions. Here we present the first CNN that can segment thalamic nuclei from T1 and diffusion data of any resolution without retraining or fine tuning. Our method builds on a public histological atlas of the thalamic nuclei and silver standard segmentations on high-quality diffusion data obtained with a recent Bayesian adaptive segmentation tool. We combine these with an approximate degradation model for fast domain randomisation during training. Our CNN produces a segmentation at 0.7 mm isotropic resolution, irrespective of the resolution of the input. Moreover, it uses a parsimonious model of the diffusion signal at each voxel (fractional anisotropy and principal eigenvector) that is compatible with virtually any set of directions and b-values, including huge amounts of legacy data. We show results of our proposed method on three heterogeneous datasets acquired on dozens of different scanners. An implementation of the method is publicly available at https://freesurfer.net/fswiki/ThalamicNucleiDTI.

Abstract: Research in the past years introduced Steered Mixture-of-Experts (SMoE) as a framework to form sparse, edge-aware models for 2D- and higher dimensional pixel data, applicable to compression, denoising, and beyond, and capable to compete with state-of-the-art compression methods. To circumvent the computationally demanding, iterative optimization method used in prior works an autoencoder design is introduced that reduces the run-time drastically while simultaneously improving reconstruction quality for block-based SMoE approaches. Coupling a deep encoder network with a shallow, parameter-free SMoE decoder enforces an efficent and explainable latent representation. Our initial work on the autoencoder design presented a simple model, with limited applicability to compression and beyond. In this paper, we build on the foundation of the first autoencoder design and improve the reconstruction quality by expanding it to models of higher complexity and different block sizes. Furthermore, we improve the noise robustness of the autoencoder for SMoE denoising applications. Our results reveal that the newly adapted autoencoders allow ultra-fast estimation of parameters for complex SMoE models with excellent reconstruction quality, both for noise free input and under severe noise. This enables the SMoE image model framework for a wide range of image processing applications, including compression, noise reduction, and super-resolution.

Abstract: Change detection from traditional optical images has limited capability to model the changes in the height or shape of objects. Change detection using 3D point cloud aerial LiDAR survey data can fill this gap by providing critical depth information. While most existing machine learning based 3D point cloud change detection methods are supervised, they severely depend on the availability of annotated training data, which is in practice a critical point. To circumnavigate this dependence, we propose an unsupervised 3D point cloud change detection method mainly based on self-supervised learning using deep clustering and contrastive learning. The proposed method also relies on an adaptation of deep change vector analysis to 3D point cloud via nearest point comparison. Experiments conducted on a publicly available real dataset show that the proposed method obtains higher performance in comparison to the traditional unsupervised methods, with a gain of about 9% in mean accuracy (to reach more than 85%). Thus, it appears to be a relevant choice in scenario where prior knowledge (labels) is not ensured.

Abstract: For invasive breast cancer, immunohistochemical (IHC) techniques are often used to detect the expression level of human epidermal growth factor receptor-2 (HER2) in breast tissue to formulate a precise treatment plan. From the perspective of saving manpower, material and time costs, directly generating IHC-stained images from hematoxylin and eosin (H&E) stained images is a valuable research direction. Therefore, we held the breast cancer immunohistochemical image generation challenge, aiming to explore novel ideas of deep learning technology in pathological image generation and promote research in this field. The challenge provided registered H&E and IHC-stained image pairs, and participants were required to use these images to train a model that can directly generate IHC-stained images from corresponding H&E-stained images. We selected and reviewed the five highest-ranking methods based on their PSNR and SSIM metrics, while also providing overviews of the corresponding pipelines and implementations. In this paper, we further analyze the current limitations in the field of breast cancer immunohistochemical image generation and forecast the future development of this field. We hope that the released dataset and the challenge will inspire more scholars to jointly study higher-quality IHC-stained image generation.

Abstract: Accurately segmenting blood vessels in retinal fundus images is crucial in the early screening, diagnosing, and evaluating some ocular diseases. However, significant light variations and non-uniform contrast in these images make segmentation quite challenging. Thus, this paper employ an attention fusion mechanism that combines the channel attention and spatial attention mechanisms constructed by Transformer to extract information from retinal fundus images in both spatial and channel dimensions. To eliminate noise from the encoder image, a spatial attention mechanism is introduced in the skip connection. Moreover, a Dropout layer is employed to randomly discard some neurons, which can prevent overfitting of the neural network and improve its generalization performance. Experiments were conducted on publicly available datasets DERIVE, STARE, and CHASEDB1. The results demonstrate that our method produces satisfactory results compared to some recent retinal fundus image segmentation algorithms.

Abstract: Due to the flexibility of prompting, foundation models have become the dominant force in the domains of natural language processing and image generation. With the recent introduction of the Segment Anything Model (SAM), the prompt-driven paradigm has entered the realm of image segmentation, bringing with a range of previously unexplored capabilities. However, it remains unclear whether it can be applicable to medical image segmentation due to the significant differences between natural images and medical images. In this report, we summarize recent efforts to extend the success of SAM to medical image segmentation tasks, including both empirical benchmarking and methodological adaptations, and discuss potential future directions for SAM in medical image segmentation. We also set up a collection of literature reviews to boost the research on this topic at https://github.com/YichiZhang98/SAM4MIS.