Embedding-based Instance Segmentation of Microscopy Images

• URL: http://arxiv.org/abs/2101.10033v1
• Date: Mon, 25 Jan 2021 12:06:44 GMT
• Title: Embedding-based Instance Segmentation of Microscopy Images
• Authors: Manan Lalit, Pavel Tomancak, Florian Jug
• Abstract summary: We introduce EmbedSeg, an end-to-end trainable deep learning method based on the work by Neven et al. While their approach embeds each pixel to the centroid of any given instance, in EmbedSeg, motivated by the complex shapes of biological objects, we propose to use the medoid instead. We demonstrate that embedding-based instance segmentation achieves competitive results in comparison to state-of-the-art methods on diverse microscopy datasets.
• Score: 8.516639438995785
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Automatic detection and segmentation of objects in microscopy images is important for many biological applications. In the domain of natural images, and in particular in the context of city street scenes, embedding-based instance segmentation leads to high-quality results. Inspired by this line of work, we introduce EmbedSeg, an end-to-end trainable deep learning method based on the work by Neven et al. While their approach embeds each pixel to the centroid of any given instance, in EmbedSeg, motivated by the complex shapes of biological objects, we propose to use the medoid instead. Additionally, we make use of a test-time augmentation scheme, and show that both suggested modifications improve the instance segmentation performance on biological microscopy datasets notably. We demonstrate that embedding-based instance segmentation achieves competitive results in comparison to state-of-the-art methods on diverse and biologically relevant microscopy datasets. Finally, we show that the overall pipeline has a small enough memory footprint to be used on virtually all CUDA enabled laptop hardware. Our open-source implementation is available at github.com/juglab/EmbedSeg.

Related papers

• Unsupervised Learning of Object-Centric Embeddings for Cell Instance Segmentation in Microscopy Images [3.039768384237206]
  We introduce object-centric embeddings (OCEs) OCEs embed image patches such that the offsets between patches cropped from the same object are preserved. We show theoretically that OCEs can be learnt through a self-supervised task that predicts the spatial offset between image patches.
  arXiv  Detail & Related papers  (2023-10-12T16:59:50Z)
• Microscopy Image Segmentation via Point and Shape Regularized Data Synthesis [9.47802391546853]
  We develop a unified pipeline for microscopy image segmentation using synthetically generated training data. Our framework achieves comparable results to models trained on authentic microscopy images with dense labels.
  arXiv  Detail & Related papers  (2023-08-18T22:00:53Z)
• Deep Learning-based Bio-Medical Image Segmentation using UNet Architecture and Transfer Learning [0.0]
  We implement UNet architecture from scratch and evaluate its performance on biomedical image datasets. We show that transferred learning model has better performance in image segmentation than UNet model that is implemented from scratch.
  arXiv  Detail & Related papers  (2023-05-24T07:45:54Z)
• De-coupling and De-positioning Dense Self-supervised Learning [65.56679416475943]
  Dense Self-Supervised Learning (SSL) methods address the limitations of using image-level feature representations when handling images with multiple objects. We show that they suffer from coupling and positional bias, which arise from the receptive field increasing with layer depth and zero-padding. We demonstrate the benefits of our method on COCO and on a new challenging benchmark, OpenImage-MINI, for object classification, semantic segmentation, and object detection.
  arXiv  Detail & Related papers  (2023-03-29T18:07:25Z)
• SOLO: A Simple Framework for Instance Segmentation [84.00519148562606]
  "instance categories" assigns categories to each pixel within an instance according to the instance's location. "SOLO" is a simple, direct, and fast framework for instance segmentation with strong performance. Our approach achieves state-of-the-art results for instance segmentation in terms of both speed and accuracy.
  arXiv  Detail & Related papers  (2021-06-30T09:56:54Z)
• Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization [112.68171734288237]
  We propose a novel framework for discriminative pixel-level tasks using a generative model of both images and labels. We learn a generative adversarial network that captures the joint image-label distribution and is trained efficiently using a large set of unlabeled images. We demonstrate strong in-domain performance compared to several baselines, and are the first to showcase extreme out-of-domain generalization.
  arXiv  Detail & Related papers  (2021-04-12T21:41:25Z)
• Sparse Object-level Supervision for Instance Segmentation with Pixel Embeddings [4.038011160363972]
  Most state-of-the-art instance segmentation methods have to be trained on densely annotated images. We propose a proposal-free segmentation approach based on non-spatial embeddings. We evaluate the proposed method on challenging 2D and 3D segmentation problems in different microscopy modalities.
  arXiv  Detail & Related papers  (2021-03-26T16:36:56Z)
• Attention-Based Transformers for Instance Segmentation of Cells in Microstructures [22.215852332444904]
  We present a novel attention-based cell detection transformer (Cell-DETR) for direct end-to-end instance segmentation. While the segmentation performance is on par with a state-of-the-art instance segmentation method, Cell-DETR is simpler and faster. For the specific use case, the proposed method surpasses the state-of-the-art tools for semantic segmentation and additionally predicts the individual object instances.
  arXiv  Detail & Related papers  (2020-11-19T10:49:56Z)
• Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation [67.88276573341734]
  We propose a new method for unseen object instance segmentation by learning RGB-D feature embeddings from synthetic data. A metric learning loss function is utilized to learn to produce pixel-wise feature embeddings. We further improve the segmentation accuracy with a new two-stage clustering algorithm.
  arXiv  Detail & Related papers  (2020-07-30T00:23:07Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)
• Panoptic Feature Fusion Net: A Novel Instance Segmentation Paradigm for Biomedical and Biological Images [91.41909587856104]
  We present a Panoptic Feature Fusion Net (PFFNet) that unifies the semantic and instance features in this work. Our proposed PFFNet contains a residual attention feature fusion mechanism to incorporate the instance prediction with the semantic features. It outperforms several state-of-the-art methods on various biomedical and biological datasets.
  arXiv  Detail & Related papers  (2020-02-15T09:19:41Z)

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.