HIVE-Net: Centerline-aware hierarchical view-ensemble convolutional network for mitochondria segmentation in EM images

Yuan, Zhimin; Ma, Xiaofen; Yi, Jiajin; Luo, Zhengrong

doi:10.1016/j.cmpb.2020.105925

Cited by 17 publications

(16 citation statements)

References 15 publications

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“…Figure 5 provides visual comparisons of the proposed method with two strong baselines, i.e., 2D U-Net (Ronneberger et al, 2015 ) and 3D U-Net (Çiçek et al, 2016 ) and a state-of-the-art 3D model, i.e., HIVE-Net (Yuan et al, 2021 ), on examples in EPFL dataset and Kasthuri++ dataset. In comparison of the results in Figures 5B,C,E , we can see that the proposed method obviously shows fewer false detections and fewer missed detections than 2D U-Net and 3D U-Net.…”

Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Recently, various methods (Lucchi et al, 2013 ; Cheng and Varshney, 2017 ; Cetina et al, 2018 ; Xiao et al, 2018 ; Casser et al, 2020 ; Peng and Yuan, 2020 ; Yuan et al, 2021 ) have been introduced to address mitochondria segmentation. According to the features they used, mitochondria segmentation can be categorized into two classes: traditional methods with hand-crafted features (Lucchi et al, 2011 , 2013 ; Cetina et al, 2018 ; Peng and Yuan, 2020 ) and deep learning methods with automatically learned features (Cheng and Varshney, 2017 ; Xiao et al, 2018 ; Casser et al, 2020 ; Yuan et al, 2020 , 2021 ). Generally speaking, deep-learning-based methods, especially methods based on fully convolutional neural networks (Ronneberger et al, 2015 ; Litjens et al, 2017 ; Shelhamer et al, 2017 ), show better performance than traditional machine learning and computer vision methods (Lucchi et al, 2013 ; Cetina et al, 2018 ; Peng and Yuan, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…Since EM images are volumetric data, both 3D models and 2D models have been adopted in each class. Typically, 3D models (Çiçek et al, 2016 ; Xiao et al, 2018 ; Yuan et al, 2021 ) show better performance by taking advantage of full spatial contexts but at the expense of high computational cost; in contrast, 2D models (Ronneberger et al, 2015 ; Casser et al, 2020 ) are more computationally efficient but may neglect inter-slice consistency and show inferior performance. However, 2D methods are flexible to process EM images with large slice thickness.…”

Section: Introductionmentioning

confidence: 99%

“…Xiao et al ( 2018 ) proposed an effective approach using a modified 3D U-Net, a 3D residual convolutional network with deep supervision. Yuan et al ( 2021 ) introduced a lightweight HIVE-Net with state-of-the-art performance. Their method conducted 3D segmentation but essentially with only more computationally-efficient 2D convolutions.…”

Section: Introductionmentioning

confidence: 99%

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

2021

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Semantic segmentation of mitochondria from electron microscopy (EM) images is an essential step to obtain reliable morphological statistics about mitochondria. However, automatically delineating plenty of mitochondria of varied shapes from complex backgrounds with sufficient accuracy is challenging. To address these challenges, we develop a hierarchical encoder-decoder network (HED-Net), which has a three-level nested U-shape architecture to capture rich contextual information. Given the irregular shape of mitochondria, we introduce a novel soft label-decomposition strategy to exploit shape knowledge in manual labels. Rather than simply using the ground truth label maps as the unique supervision in the model training, we introduce additional subcategory-aware supervision by softly decomposing each manual label map into two complementary label maps according to mitochondria's ovality. The three label maps are integrated with our HED-Net to supervise the model training. While the original label map guides the network to segment all the mitochondria of varied shapes, the auxiliary label maps guide the network to segment subcategories of mitochondria of circular shape and elliptic shape, respectively, which are much more manageable tasks. Extensive experiments on two public benchmarks show that our HED-Net performs favorably against state-of-the-art methods.

show abstract

Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2021

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Mask Rearranging Data Augmentation for 3D Mitochondria Segmentation

Chen

et al. 2022

Lecture Notes in Computer Science

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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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HIVE-Net: Centerline-aware hierarchical view-ensemble convolutional network for mitochondria segmentation in EM images

Cited by 17 publications

References 15 publications

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

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

Mask Rearranging Data Augmentation for 3D Mitochondria Segmentation

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

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