Electron microscopy (EM) image denoising is critical for visualization and subsequent analysis. Despite the remarkable achievements of deep learning-based non-blind denoising methods, their performance drops significantly when domain shifts exist between the training and testing data. To address this issue, unpaired blind denoising methods have been proposed. However, these methods heavily rely on image-to-image translation and neglect the inherent characteristics of EM images, limiting their overall denoising performance. In this paper, we propose the first unsupervised domain adaptive EM image denoising method, which is grounded in the observation that EM images from similar samples share common content characteristics. Specifically, we first disentangle the content representations and the noise components from noisy images and establish a shared domain-agnostic content space via domain alignment to bridge the synthetic images (source domain) and the real images (target domain). To ensure precise domain alignment, we further incorporate domain regularization by enforcing that: the pseudo-noisy images, reconstructed using both content representations and noise components, accurately capture the characteristics of the noisy images from which the noise components originate, all while maintaining semantic consistency with the noisy images from which the content representations originate. To guarantee lossless representation decomposition and image reconstruction, we introduce disentanglement-reconstruction invertible networks. Finally, the reconstructed pseudo-noisy images, paired with their corresponding clean counterparts, serve as valuable training data for the denoising network. Extensive experiments on synthetic and real EM datasets demonstrate the superiority of our method in terms of image restoration quality and downstream neuron segmentation accuracy. Our code is publicly available at https://github.com/sydeng99/DADn.

As the mainstream technique for capturing images of biological specimens at nanometer resolution, electron microscopy (EM) is extremely time-consuming for scanning wide field-of-view (FOV) specimens. In this paper, we investigate a challenging task of large-factor EM image super-resolution (EMSR), which holds great promise for reducing scanning time, relaxing acquisition conditions, and expanding imaging FOV. By exploiting the repetitive structures and volumetric coherence of EM images, we propose the first generative learning-based framework for large-factor EMSR. Specifically, motivated by the predictability ofrepetitive structures and textures in EM images, we first learn a discrete codebook in the latent space to represent highresolution (HR) cell-specific priors and a latent vector indexer to map low-resolution (LR) EM images to their corresponding latent vectors in a generative manner. By incorporating the generative cell-specific priors from HR EM images through a multi-scale prior fusion module, we then deploy multi-image feature alignment and fusion to further exploit the inter-section coherence in the volumetric EM data. Extensive experiments demonstrate that our proposed framework outperforms advanced single-image and video super-resolution methods for 8× and 16× EMSR (i.e., with 64 times and 256 times less data acquired, respectively), achieving superior visual reconstruction quality and down-stream segmentation accuracy on benchmark EM datasets. Code is available at https://github.com/jtshou/GPEMSR.

Electron microscopy (EM) images are notoriously challenging to segment due to their complex structures and lack of effective annotations. Fortunately, large-scale self-supervised pretraining offers a promising solution by allowing us to acquire prior knowledge of cell and subcellular tissue structures, which can significantly improve EM instance segmentation results. However, most existing pretraining methods fail to capture the crucial local information that is essential for EM images, instead focusing only on high-level semantic information. In this paper, we propose a novel pretraining framework that leverages multiscale visual representations to adapt to the complex structures of EM images. Our framework achieves instance-level alignment by maximizing the consistency between strongly and weakly augmented images, while also incorporating a cross-attention mechanism to match multiscale features and encode more low-level information into high-level semantics. Most importantly, our approach employs multi-task optimization on the feature pyramid, enabling multiscale pixel restoration and feature comparison. We extensively pretrain our method on four large-scale EM datasets and demonstrate significant gains on neuron and mitochondria segmentation tasks. Code is available at https://github.com/ydchen0806/MS-Con-EM-Seg.

The size of image volumes in connectomics studies now reaches terabyte and often petabyte scales with a great diversity of appearance due to different sample preparation procedures. However, manual annotation of neuronal structures (e.g., synapses) in these huge image volumes is time-consuming, leading to limited labeled training data often smaller than 0.001% of the large-scale image volumes in application. Methods that can utilize in-domain labeled data and generalize to out-of-domain unlabeled data are in urgent need. Although many domain adaptation approaches are proposed to address such issues in the natural image domain, few of them have been evaluated on connectomics data due to a lack of domain adaptation benchmarks. Therefore, to enable developments of domain adaptive synapse detection methods for large-scale connectomics applications, we annotated 14 image volumes from a biologically diverse set of Megaphragma viggianii brain regions originating from three different whole-brain datasets and organized the WASPSYN challenge at ISBI 2023. The annotations include coordinates of pre-synapses and post-synapses in the 3D space, together with their one-to-many connectivity information. This paper describes the dataset, the tasks, the proposed baseline, the evaluation method, and the results of the challenge. Limitations of the challenge and the impact on neuroscience research are also discussed. The challenge is and will continue to be available at https://codalab.lisn.upsaclay.fr/competitions/9169. Successful algorithms that emerge from our challenge may potentially revolutionize real-world connectomics research and further the cause that aims to unravel the complexity of brain structure and function.

Existing semantic segmentation methods improve generalization capability, by regularizing various images to a canonical feature space. While this process contributes to generalization, it weakens the representation inevitably. In contrast to existing methods, we instead utilize the difference between images to build a better representation space, where the distinct style features are extracted and stored as the bases of representation. Then, the generalization to unseen image styles is achieved by projecting features to this known space. Specifically, we realize the style projection as a weighted combination of stored bases, where the similarity distances are adopted as the weighting factors. Based on the same concept, we extend this process to the decision part of model and promote the generalization of semantic prediction. By measuring the similarity distances to semantic bases (i.e., prototypes), we replace the common deterministic prediction with semantic clustering. Comprehensive experiments demonstrate the advantage of proposed method to the state of the art, up to 3.6% mIoU improvement in average on unseen scenarios. Code and models are available at https://gitee.com/mindspore/models/tree/master/research/cv/SPC-Net.

Image resampling is a basic technique that is widely employed in daily applications. Existing deep neural networks (DNNs) have made impressive progress in resampling performance. Yet these methods are still not the perfect substitute for interpolation, due to the issues of efficiency and continuous resampling. In this work, we propose a novel method of Learning Resampling Function (termed LeRF), which takes advantage of both the structural priors learned by DNNs and the locally continuous assumption of interpolation methods. Specifically, LeRF assigns spatially-varying steerable resampling functions to input image pixels and learns to predict the hyper-parameters that determine the orientations of these resampling functions with a neural network. To achieve highly efficient inference, we adopt look-up tables (LUTs) to accelerate the inference of the learned neural network. Furthermore, we design a directional ensemble strategy and edge-sensitive indexing patterns to better capture local structures. Extensive experiments show that our method runs as fast as interpolation, generalizes well to arbitrary transformations, and outperforms interpolation significantly, e.g., up to 3dB PSNR gain over bicubic for x 2 upsampling on Manga109.

Neuron reconstruction in a full adult fly brain from high-resolution electron microscopy (EM) data is regarded as a cornerstone for neuroscientists to explore how neurons inspire intelligence. As the central part of neurons, somas in the full brain indicate the origin of neurogenesis and neural functions. However, due to the absence of EM datasets specifically annotated for somas, existing deep learning-based neuron reconstruction methods cannot directly provide accurate soma distribution and morphology. Moreover, full brain neuron reconstruction remains extremely time-consuming due to the unprecedentedly large size of EM data. In this paper, we develop an efficient soma reconstruction method for obtaining accurate soma distribution and morphology information in a full adult fly brain. To this end, we first make a high-resolution EM dataset with fine-grained 3D manual annotations on somas. Relying on this dataset, we propose an efficient, two-stage deep learning algorithm for predicting accurate locations and boundaries of 3D soma instances. Further, we deploy a parallelized, high-throughput data processing pipeline for executing the above algorithm on the full brain. Finally, we provide quantitative and qualitative benchmark comparisons on the testset to validate the superiority of the proposed method, as well as preliminary statistics of the reconstructed somas in the full adult fly brain from the biological perspective. We release our code and dataset at https://github.com/liuxy1103/EMADS.

Sparse instance-level supervision has recently been explored to address insufficient annotation in biomedical instance segmentation, which is easier to annotate crowded instances and better preserves instance completeness for 3D volumetric datasets compared to common semi-supervision. In this paper, we propose a sparsely supervised biomedical instance segmentation framework via cross-representation affinity consistency regularization. Specifically, we adopt two individual networks to enforce the perturbation consistency between an explicit affinity map and an implicit affinity map to capture both feature-level instance discrimination and pixel-level instance boundary structure. We then select the highly confident region of each affinity map as the pseudo label to supervise the other one for affinity consistency learning. To obtain the highly confident region, we propose a pseudo-label noise filtering scheme by integrating two entropy-based decision strategies. Extensive experiments on four biomedical datasets with sparse instance annotations show the state-of-the-art performance of our proposed framework. For the first time, we demonstrate the superiority of sparse instance-level supervision on 3D volumetric datasets, compared to common semi-supervision under the same annotation cost. Code is available at https://github.com/liuxy1103/CRAC.

The performance of existing supervised neuron segmentation methods is highly dependent on the number of accurate annotations, especially when applied to large scale electron microscopy (EM) data. By extracting semantic information from unlabeled data, self-supervised methods can improve the performance of downstream tasks, among which the mask image model (MIM) has been widely used due to its simplicity and effectiveness in recovering original information from masked images. However, due to the high degree of structural locality in EM images, as well as the existence of considerable noise, many voxels contain little discriminative information, making MIM pretraining inefficient on the neuron segmentation task. To overcome this challenge, we propose a decision-based MIM that utilizes reinforcement learning (RL) to automatically search for optimal image masking ratio and masking strategy. Due to the vast exploration space, using single-agent RL for voxel prediction is impractical. Therefore, we treat each input patch as an agent with a shared behavior policy, allowing for multi-agent collaboration. Furthermore, this multi-agent model can capture dependencies between voxels, which is beneficial for the downstream segmentation task. Experiments conducted on representative EM datasets demonstrate that our approach has a significant advantage over alternative self-supervised methods on the task of neuron segmentation. Code is available at https://github.com/ydchen0806/dbMiM.

Homogeneous instance segmentation aims to identify each instance in an image where all interested instances belong to the same category, such as plant leaves and microscopic cells. Recently, proposal-free methods, which straightforwardly generate instance-aware information to group pixels into different instances, have received increasing attention due to their efficient pipeline. However, they often fail to distinguish adjacent instances due to similar appearances, dense distribution and ambiguous boundaries of instances in homogeneous images. In this paper, we propose a pixel-embedded affinity modeling method for homogeneous instance segmentation, which is able to preserve the semantic information of instances and improve the distinguishability of adjacent instances. Instead of predicting affinity directly, we propose a self-correlation module to explicitly model the pairwise relationships between pixels, by estimating the similarity between embeddings generated from the input image through CNNs. Based on the self-correlation module, we further design a cross-correlation module to maintain the semantic consistency between instances. Specifically, we map the transformed input images with different views and appearances into the same embedding space, and then mutually estimate the pairwise relationships of embeddings generated from the original input and its transformed variants. In addition, to integrate the global instance information, we introduce an embedding pyramid module to model affinity on different scales. Extensive experiments demonstrate the versatile and superior performance of our method on three representative datasets. Code and models are available at https://github.com/weih527/Pixel-Embedded-Affinity.

Recent advanced proposal-free instance segmentation methods have made significant progress in biological images. However, existing methods are vulnerable to local imaging artifacts and similar object appearances, resulting in over-merge and over-segmentation. To reduce these two kinds of errors, we propose a new biological instance segmentation framework based on a superpixel-guided graph, which consists of two stages, i.e., superpixel-guided graph construction and superpixel agglomeration. Specifically, the first stage generates enough superpixels as graph nodes to avoid over-merge, and extracts node and edge features to construct an initialized graph. The second stage agglomerates superpixels into instances based on the relationship of graph nodes predicted by a graph neural network (GNN). To solve over-segmentation and prevent introducing additional over-merge, we specially design two loss functions to supervise the GNN, i.e., a repulsion-attraction (RA) loss to better distinguish the relationship of nodes in the feature space, and a maximin agglomeration score (MAS) loss to pay more attention to crucial edge classification. Extensive experiments on three representative biological datasets demonstrate the superiority of our method over existing state-of-the-art methods. Code is available at https://github.com/liuxy1103/BISSG.

Serial section transmission electron micro-scopy (ssTEM) reveals biological information at a scale of nanometer and plays an important role in the ultrastructural analysis. However, due to the imperfect preparation of biological samples, ssTEM images are usually degraded with various artifacts that greatly challenge the subsequent analysis and visualization. In this paper, we introduce a unified deep learning framework for ssTEM image restoration which addresses three main types of artifacts, i.e., Support Film Folds (SFF), Staining Precipitates (SP), and Missing Sections (MS). To achieve this goal, we first model the appearance of SFF and SP artifacts by conducting comprehensive analyses on the statistics of real degraded images, relying on which we can then simulate a large number of paired images (degraded/artifacts-free) for training a deep restoration network. Then, we design a coarse-to-fine restoration network consisting of three modules, i.e., interpolation, correction, and fusion. The interpolation module exploits the adjacent artifacts-free images for an initial restoration, while the correction module resorts to the degraded image itself to rectify the artifacts. Finally, the fusion module jointly utilizes the above two results to further improve the restoration fidelity. Experimental results on both synthetic and real test data validate the significantly improved performance of our proposed framework over existing solutions, in terms of both image restoration fidelity and neuron segmentation accuracy. To the best of our knowledge, this is the first unified deep learning framework for ssTEM image restoration from different types of artifacts. Code is available at https://github.com/sydeng99/ssTEM-restoration.

Emerging deep learning-based methods have enabled great progress in automatic neuron segmentation from Electron Microscopy (EM) volumes. However, the success of existing methods is heavily reliant upon a large number of annotations that are often expensive and time-consuming to collect due to dense distributions and complex structures of neurons. If the required quantity of manual annotations for learning cannot be reached, these methods turn out to be fragile. To address this issue, in this article, we propose a two-stage, semi-supervised learning method for neuron segmentation to fully extract useful information from unlabeled data. First, we devise a proxy task to enable network pre-training by reconstructing original volumes from their perturbed counterparts. This pre-training strategy implicitly extracts meaningful information on neuron structures from unlabeled data to facilitate the next stage of learning. Second, we regularize the supervised learning process with the pixel-level prediction consistencies between unlabeled samples and their perturbed counterparts. This improves the generalizability of the learned model to adapt diverse data distributions in EM volumes, especially when the number of labels is limited. Extensive experiments on representative EM datasets demonstrate the superior performance of our reinforced consistency learning compared to supervised learning, i.e., up to 400% gain on the VOI metric with only a few available labels. This is on par with a model trained on ten times the amount of labeled data in a supervised manner. Code is available at https://github.com/weih527/SSNS-Net.