Hierarchical Encoder-Decoder With Soft Label-Decomposition for Mitochondria Segmentation in EM Images

Luo, Zhengrong; Wang, Ye; Liu, Shikun

doi:10.3389/fnins.2021.687832

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…U-Net++ [49] proposes a combination of architectural improvements by improving the skip connection pathways and extending the deep supervision mechanism, while Tiramisu [50] employs DenseNets [51] instead of ResNets [34] improving the performance using fewer parameters. Other approaches [11,52] propose hierarchical decoding for segmentation, which, instead of employing the pipeline of downsampling and upsampling path, decode features at multiple resolutions and combine them at the end.…”

Section: Related Workmentioning

confidence: 99%

Radio astronomical images object detection and segmentation: A benchmark on deep learning methods

Renato¹,

Magro²,

Fiameni³

et al. 2023

Preprint

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In recent years, deep learning has been successfully applied in various scientific domains. Following these promising results and performances, it has recently also started being evaluated in the domain of radio astronomy. In particular, since radio astronomy is entering the Big Data era, with the advent of the largest telescope in the world -the Square Kilometre Array (SKA), the task of automatic object detection and instance segmentation is crucial for source finding and analysis. In this work, we explore the performance of the most affirmed deep learning approaches, applied to astronomical images obtained by radio interferometric instrumentation, to solve the task of automatic source detection. This is carried out by applying models designed to accomplish two different kinds of tasks: object detection and semantic segmentation. The goal is

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Section: Related Workmentioning

confidence: 99%

Radio astronomical images object detection and segmentation: A benchmark on deep learning methods

Renato¹,

Magro²,

Fiameni³

et al. 2023

Preprint

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show abstract

“…Previous methods for mitochondria segmentation have primarily used handcrafted features [4] or those derived using supervised learning to encode images [5,6]. The success of encoder-decoder architectures such as FCN, U-Net, and DeepLabv3+ for semantic segmentation, has enabled pixel-wise classification of EM images.…”

Section: Related Workmentioning

confidence: 99%

COFI - Coarse-Semantic to Fine-Instance Unsupervised Mitochondria Segmentation in EM

Aswath,

Alsahaf,

Westenbrink

et al. 2023

Lecture Notes in Computer Science

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Instance segmentation is crucial for insightful analysis in the increasing use of large-scale electron microscopy (EM) to gain a better understanding of disease causes or progression. Instance segmentation is a more granular version of semantic segmentation, as it identifies and distinguishes individual object instances, whereas semantic segmentation only identifies object classes. In this study, we introduce a two-stage unsupervised approach called COFI, which stands for Coarse-Semantic to Fine-Instance segmentation, for the application of mitochondria segmentation in large-scale 2D EM images. In its first stage, it produces a rough region mask by clustering image patches and prompting a user to select the regions of interest. This is followed by a boundary delineation method based on the brain-inspired COSFIRE filter which is augmented by an inhibition component that makes it robust to image texture and noise. The effectiveness of the proposed COFI approach is evaluated on an EM dataset of the heart muscle of a mouse tissue, which consisted of four tiles of 16384 × 16384 pixels, containing a total of 2287 instances of mitochondria among other subcellular structures. It consistently achieved panoptic quality measures that are substantially superior to competing supervised methodologies. Besides its elevated effectiveness, the proposed COFI approach is conceptually simple and sufficiently versatile as the structure of interest is not intrinsic to the method.

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“…Thanks to the Electron microscopy (EM) technique, high-resolution images of mitochondria and other cellular structures are now available, making them a valuable resource for studying cellular biology and connectomics (Casser et al 2020;Wei et al 2020;Lucchi et al 2011). The utilization of deep learning algorithms in mitochondria segmentation has shown significant progress, as demonstrated by state-of-the-art (SOTA) methods (Luo et al 2021;Peng, Yi, and Yuan 2020;Peng and Yuan 2019;Yuan et al 2021). Most of these techniques employ the U-Net (Ronneberger, Fischer, and Brox 2015) architecture or its variations (Casser et al 2020;Mekuč et al 2020) to address the unique challenges posed by EM image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Evidential Uncertainty-Guided Mitochondria Segmentation for 3D EM Images

Shi,

Duan,

Huang

et al. 2024

AAAI

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Recent advances in deep learning have greatly improved the segmentation of mitochondria from Electron Microscopy (EM) images. However, suffering from variations in mitochondrial morphology, imaging conditions, and image noise, existing methods still exhibit high uncertainty in their predictions. Moreover, in view of our findings, predictions with high levels of uncertainty are often accompanied by inaccuracies such as ambiguous boundaries and amount of false positive segments. To deal with the above problems, we propose a novel approach for mitochondria segmentation in 3D EM images that leverages evidential uncertainty estimation, which for the first time integrates evidential uncertainty to enhance the performance of segmentation. To be more specific, our proposed method not only provides accurate segmentation results, but also estimates associated uncertainty. Then, the estimated uncertainty is used to help improve the segmentation performance by an uncertainty rectification module, which leverages uncertainty maps and multi-scale information to refine the segmentation. Extensive experiments conducted on four challenging benchmarks demonstrate the superiority of our proposed method over existing approaches.

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Hierarchical Encoder-Decoder With Soft Label-Decomposition for Mitochondria Segmentation in EM Images

Cited by 8 publications

References 22 publications

Radio astronomical images object detection and segmentation: A benchmark on deep learning methods

Radio astronomical images object detection and segmentation: A benchmark on deep learning methods

COFI - Coarse-Semantic to Fine-Instance Unsupervised Mitochondria Segmentation in EM

Evidential Uncertainty-Guided Mitochondria Segmentation for 3D EM Images

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