Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images

• URL: http://arxiv.org/abs/2310.14226v1
• Date: Sun, 22 Oct 2023 08:11:08 GMT
• Title: Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images
• Authors: Wei Lou and Xinyi Yu and Chenyu Liu and Xiang Wan and Guanbin Li and Siqi Liu and Haofeng Li
• Abstract summary: We develop an automatic cell classification pipeline to label microscopy images. We then train a classification model based on the category labels. We deploy two types of segmentation models to segment cells with roundish and irregular shapes.
• Score: 66.79688768141814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cell segmentation for multi-modal microscopy images remains a challenge due to the complex textures, patterns, and cell shapes in these images. To tackle the problem, we first develop an automatic cell classification pipeline to label the microscopy images based on their low-level image characteristics, and then train a classification model based on the category labels. Afterward, we train a separate segmentation model for each category using the images in the corresponding category. Besides, we further deploy two types of segmentation models to segment cells with roundish and irregular shapes respectively. Moreover, an efficient and powerful backbone model is utilized to enhance the efficiency of our segmentation model. Evaluated on the Tuning Set of NeurIPS 2022 Cell Segmentation Challenge, our method achieves an F1-score of 0.8795 and the running time for all cases is within the time tolerance.

Related papers

• Practical Guidelines for Cell Segmentation Models Under Optical Aberrations in Microscopy [14.042884268397058]
  This study evaluates cell image segmentation models under optical aberrations from fluorescence and bright field microscopy. We train and test several segmentation models, including the Otsu threshold method and Mask R-CNN with different network heads. In contrast, Cellpose 2.0 proves effective for complex cell images under similar conditions.
  arXiv  Detail & Related papers  (2024-04-12T15:45:26Z)
• CellMixer: Annotation-free Semantic Cell Segmentation of Heterogeneous Cell Populations [9.335273591976648]
  We present CellMixer, an innovative annotation-free approach for the semantic segmentation of heterogeneous cell populations. Our results show that CellMixer can achieve competitive segmentation performance across multiple cell types and imaging modalities.
  arXiv  Detail & Related papers  (2023-12-01T15:50:20Z)
• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• Advanced Multi-Microscopic Views Cell Semi-supervised Segmentation [0.0]
  Deep learning (DL) shows powerful potential in cell segmentation tasks, but suffers from poor generalization. In this paper, we introduce a novel semi-supervised cell segmentation method called Multi-Microscopic-view Cell semi-supervised (MMCS) MMCS can train cell segmentation models utilizing less labeled multi-posture cell images with different microscopy well. It achieves an F1-score of 0.8239 and the running time for all cases is within the time tolerance.
  arXiv  Detail & Related papers  (2023-03-21T08:08:13Z)
• Distilling Ensemble of Explanations for Weakly-Supervised Pre-Training of Image Segmentation Models [54.49581189337848]
  We propose a method to enable the end-to-end pre-training for image segmentation models based on classification datasets. The proposed method leverages a weighted segmentation learning procedure to pre-train the segmentation network en masse. Experiment results show that, with ImageNet accompanied by PSSL as the source dataset, the proposed end-to-end pre-training strategy successfully boosts the performance of various segmentation models.
  arXiv  Detail & Related papers  (2022-07-04T13:02:32Z)
• Meta Internal Learning [88.68276505511922]
  Internal learning for single-image generation is a framework, where a generator is trained to produce novel images based on a single image. We propose a meta-learning approach that enables training over a collection of images, in order to model the internal statistics of the sample image more effectively. Our results show that the models obtained are as suitable as single-image GANs for many common image applications.
  arXiv  Detail & Related papers  (2021-10-06T16:27:38Z)
• Automatic size and pose homogenization with spatial transformer network to improve and accelerate pediatric segmentation [51.916106055115755]
  We propose a new CNN architecture that is pose and scale invariant thanks to the use of Spatial Transformer Network (STN) Our architecture is composed of three sequential modules that are estimated together during training. We test the proposed method in kidney and renal tumor segmentation on abdominal pediatric CT scanners.
  arXiv  Detail & Related papers  (2021-07-06T14:50:03Z)
• Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image [31.00452985964065]
  Histological subtype of papillary (p) renal cell carcinoma (RCC), type 1 vs. type 2, is an essential prognostic factor. This paper proposes a novel instance-based Vision Transformer (i-ViT) to learn robust representations of histological images for the pRCC subtyping task. Experimental results show that the proposed method achieves better performance than existing CNN-based models with a significant margin.
  arXiv  Detail & Related papers  (2021-06-23T09:42:49Z)
• Learning to Associate Every Segment for Video Panoptic Segmentation [123.03617367709303]
  We learn coarse segment-level matching and fine pixel-level matching together. We show that our per-frame computation model can achieve new state-of-the-art results on Cityscapes-VPS and VIPER datasets.
  arXiv  Detail & Related papers  (2021-06-17T13:06:24Z)
• Learning to segment clustered amoeboid cells from brightfield microscopy via multi-task learning with adaptive weight selection [6.836162272841265]
  We introduce a novel supervised technique for cell segmentation in a multi-task learning paradigm. A combination of a multi-task loss, based on the region and cell boundary detection, is employed for an improved prediction efficiency of the network. We observe an overall Dice score of 0.93 on the validation set, which is an improvement of over 15.9% on a recent unsupervised method, and outperforms the popular supervised U-net algorithm by at least $5.8%$ on average.
  arXiv  Detail & Related papers  (2020-05-19T11:31:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.