Split and Expand: An inference-time improvement for Weakly Supervised Cell Instance Segmentation

• URL: http://arxiv.org/abs/2007.10817v3
• Date: Mon, 14 Mar 2022 05:59:46 GMT
• Title: Split and Expand: An inference-time improvement for Weakly Supervised Cell Instance Segmentation
• Authors: Lin Geng Foo, Rui En Ho, Jiamei Sun, Alexander Binder
• Abstract summary: We propose a two-step post-processing procedure, Split and Expand, to improve the conversion of segmentation maps to instances. In the Split step, we split clumps of cells from the segmentation map into individual cell instances with the guidance of cell-center predictions. In the Expand step, we find missing small cells using the cell-center predictions.
• Score: 71.50526869670716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the problem of segmenting cell nuclei instances from Hematoxylin and Eosin (H&E) stains with weak supervision. While most recent works focus on improving the segmentation quality, this is usually insufficient for instance segmentation of cell instances clumped together or with a small size. In this work, we propose a two-step post-processing procedure, Split and Expand, that directly improves the conversion of segmentation maps to instances. In the Split step, we split clumps of cells from the segmentation map into individual cell instances with the guidance of cell-center predictions through Gaussian Mixture Model clustering. In the Expand step, we find missing small cells using the cell-center predictions (which tend to capture small cells more consistently as they are trained using reliable point annotations), and utilize Layer-wise Relevance Propagation (LRP) explanation results to expand those cell-center predictions into cell instances. Our Split and Expand post-processing procedure is training-free and is executed at inference-time only. To further improve the performance of our method, a feature re-weighting loss based on LRP is proposed. We test our procedure on the MoNuSeg and TNBC datasets and show that our proposed method provides statistically significant improvements on object-level metrics. Our code will be made available.

Related papers

• LKCell: Efficient Cell Nuclei Instance Segmentation with Large Convolution Kernels [32.157968641130545]
  We propose LKCell, a high-accuracy and efficient cell segmentation method. Its core insight lies in unleashing the potential of large convolution kernels to achieve computationally efficient large receptive fields. We analyze the redundancy of previous methods and design a new segmentation decoder based on large convolution kernels.
  arXiv  Detail & Related papers  (2024-07-25T14:07:49Z)
• Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation [50.407071700154674]
  We propose to formulate annotation-efficient nucleus instance segmentation from the perspective of few-shot learning (FSL) Our work was motivated by that, with the prosperity of computational pathology, an increasing number of fully-annotated datasets are publicly accessible. Extensive experiments on a couple of publicly accessible datasets demonstrate that SGFSIS can outperform other annotation-efficient learning baselines.
  arXiv  Detail & Related papers  (2024-02-26T03:49:18Z)
• Single-cell Multi-view Clustering via Community Detection with Unknown Number of Clusters [64.31109141089598]
  We introduce scUNC, an innovative multi-view clustering approach tailored for single-cell data. scUNC seamlessly integrates information from different views without the need for a predefined number of clusters. We conducted a comprehensive evaluation of scUNC using three distinct single-cell datasets.
  arXiv  Detail & Related papers  (2023-11-28T08:34:58Z)
• CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation [36.52664417716791]
  This work considers the problem of pre-training models for few-shot cell segmentation, where massive unlabeled cell images are available but only a small proportion is annotated. We propose Cross-domain Unsupervised Pre-training, namely CUPre, transferring the capability of object detection and instance segmentation for common visual objects to the visual domain of cells using unlabeled images. Experiment shows that CUPre can outperform existing pre-training methods, achieving the highest average precision (AP) for few-shot cell segmentation and detection.
  arXiv  Detail & Related papers  (2023-10-06T02:35:31Z)
• DoNet: Deep De-overlapping Network for Cytology Instance Segmentation [7.321293750325454]
  We propose a De-overlapping Network (DoNet) in a decompose-and-recombined strategy. A Dual-path Region Module (DRM) explicitly decomposes the cell clusters into intersection and complement regions. A Mask-guided Region Proposal Strategy (MRP) integrates the cell attention maps for inner-cell instance prediction.
  arXiv  Detail & Related papers  (2023-03-25T06:26:50Z)
• Look in Different Views: Multi-Scheme Regression Guided Cell Instance Segmentation [17.633542802081827]
  We propose a novel cell instance segmentation network based on multi-scheme regression guidance. With multi-scheme regression guidance, the network has the ability to look each cell in different views. We conduct extensive experiments on benchmark datasets, DSB2018, CA2.5 and SCIS.
  arXiv  Detail & Related papers  (2022-08-17T05:24:59Z)
• BFS-Net: Weakly Supervised Cell Instance Segmentation from Bright-Field Microscopy Z-Stacks [47.72468932196169]
  We investigate the prediction of 3D cell instances from a set of BFM Z-Stack images. We propose a novel two-stage weakly supervised method for volumetric instance segmentation. We show that our approach can generalize not only to BFM Z-Stack data, but to other 3D cell imaging modalities.
  arXiv  Detail & Related papers  (2022-06-09T15:13:08Z)
• A hybrid multi-object segmentation framework with model-based B-splines for microbial single cell analysis [1.2031796234206138]
  We propose a hybrid approach for multi-object microbial cell segmentation. The approach combines an ML-based detection with a geometry-aware variational-based segmentation. We study the performance of the proposed method on time-lapse data microscopy of Corynebacterium glutamicum.
  arXiv  Detail & Related papers  (2022-05-03T08:31:55Z)
• DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution [136.7261709896713]
  We propose a data-driven approach that generates the appropriate convolution kernels to apply in response to the nature of the instances. The proposed method achieves promising results on both ScanetNetV2 and S3DIS. It also improves inference speed by more than 25% over the current state-of-the-art.
  arXiv  Detail & Related papers  (2020-11-26T14:56:57Z)
• Stretchable Cells Help DARTS Search Better [70.52254306274092]
  Differentiable neural architecture search (DARTS) has gained much success in discovering flexible and diverse cell types. Current DARTS methods are prone to wide and shallow cells, and this topology collapse induces sub-optimal searched cells. In this paper, we endowing the cells with explicit stretchability, so the search can be directly implemented on our stretchable cells.
  arXiv  Detail & Related papers  (2020-11-18T14:15:51Z)

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.