Limitation of Acyclic Oriented Graphs Matching as Cell Tracking Accuracy Measure when Evaluating Mitosis

• URL: http://arxiv.org/abs/2012.12084v1
• Date: Tue, 22 Dec 2020 15:25:47 GMT
• Title: Limitation of Acyclic Oriented Graphs Matching as Cell Tracking Accuracy Measure when Evaluating Mitosis
• Authors: Ye Chen and Yuankai Huo
• Abstract summary: Acyclic oriented graphs matching (AOGM) has been used as de facto standard evaluation metrics for cell tracking. In this paper, we exhibit the limitations of evaluating mitosis with AOGM using both simulated and real cell tracking data.
• Score: 7.076682006232971
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-object tracking (MOT) in computer vision and cell tracking in biomedical image analysis are two similar research fields, whose common aim is to achieve instance level object detection/segmentation and associate such objects across different video frames. However, one major difference between these two tasks is that cell tracking also aim to detect mitosis (cell division), which is typically not considered in MOT tasks. Therefore, the acyclic oriented graphs matching (AOGM) has been used as de facto standard evaluation metrics for cell tracking, rather than directly using the evaluation metrics in computer vision, such as multiple object tracking accuracy (MOTA), ID Switches (IDS), ID F1 Score (IDF1) etc. However, based on our experiments, we realized that AOGM did not always function as expected for mitosis events. In this paper, we exhibit the limitations of evaluating mitosis with AOGM using both simulated and real cell tracking data.

Related papers

• Trackastra: Transformer-based cell tracking for live-cell microscopy [0.0]
  Trackastra is a general purpose cell tracking approach that uses a simple transformer architecture to learn pairwise associations of cells. We show that our tracking approach performs on par with or better than highly tuned state-of-the-art cell tracking algorithms.
  arXiv  Detail & Related papers  (2024-05-24T16:44:22Z)
• Towards a Unified Transformer-based Framework for Scene Graph Generation and Human-object Interaction Detection [116.21529970404653]
  We introduce SG2HOI+, a unified one-step model based on the Transformer architecture. Our approach employs two interactive hierarchical Transformers to seamlessly unify the tasks of SGG and HOI detection. Our approach achieves competitive performance when compared to state-of-the-art HOI methods.
  arXiv  Detail & Related papers  (2023-11-03T07:25:57Z)
• Cell Tracking-by-detection using Elliptical Bounding Boxes [0.0]
  This work proposes a new approach based on the classical tracking-by-detection paradigm. It approximates the cell shapes as oriented ellipses and then uses generic-purpose oriented object detectors to identify the cells in each frame. Our results show that our method can achieve detection and tracking results competitively with state-of-the-art techniques.
  arXiv  Detail & Related papers  (2023-10-07T18:47:17Z)
• DeGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Counting [14.222014969736993]
  Multi-class cell detection and counting is an essential task for many pathological diagnoses. We propose guided posterior regularization (DeGPR) which assists an object detector by guiding it to exploit discriminative features among cells. We validate our model on two publicly available datasets, and on MuCeD, a novel dataset that we contribute.
  arXiv  Detail & Related papers  (2023-04-03T06:25:45Z)
• 3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D Point Clouds [95.54285993019843]
  We propose a method for joint detection and tracking of multiple objects in 3D point clouds. Our model exploits temporal information employing multiple frames to detect objects and track them in a single network.
  arXiv  Detail & Related papers  (2022-11-01T20:59:38Z)
• End-to-end Tracking with a Multi-query Transformer [96.13468602635082]
  Multiple-object tracking (MOT) is a challenging task that requires simultaneous reasoning about location, appearance, and identity of the objects in the scene over time. Our aim in this paper is to move beyond tracking-by-detection approaches, to class-agnostic tracking that performs well also for unknown object classes.
  arXiv  Detail & Related papers  (2022-10-26T10:19:37Z)
• Unified Transformer Tracker for Object Tracking [58.65901124158068]
  We present the Unified Transformer Tracker (UTT) to address tracking problems in different scenarios with one paradigm. A track transformer is developed in our UTT to track the target in both Single Object Tracking (SOT) and Multiple Object Tracking (MOT)
  arXiv  Detail & Related papers  (2022-03-29T01:38:49Z)
• Persistent Animal Identification Leveraging Non-Visual Markers [71.14999745312626]
  We aim to locate and provide a unique identifier for each mouse in a cluttered home-cage environment through time. This is a very challenging problem due to (i) the lack of distinguishing visual features for each mouse, and (ii) the close confines of the scene with constant occlusion. Our approach achieves 77% accuracy on this animal identification problem, and is able to reject spurious detections when the animals are hidden.
  arXiv  Detail & Related papers  (2021-12-13T17:11:32Z)
• Graph Attention Tracking [76.19829750144564]
  We propose a simple target-aware Siamese graph attention network for general object tracking. Experiments on challenging benchmarks including GOT-10k, UAV123, OTB-100 and LaSOT demonstrate that the proposed SiamGAT outperforms many state-of-the-art trackers.
  arXiv  Detail & Related papers  (2020-11-23T04:26:45Z)
• Cell Segmentation and Tracking using CNN-Based Distance Predictions and a Graph-Based Matching Strategy [0.20999222360659608]
  We present a method for the segmentation of touching cells in microscopy images. By using a novel representation of cell borders, inspired by distance maps, our method is capable to utilize not only touching cells but also close cells in the training process. This representation is notably robust to annotation errors and shows promising results for the segmentation of microscopy images containing in the training data underrepresented or not included cell types.
  arXiv  Detail & Related papers  (2020-04-03T11:55:28Z)
• MPM: Joint Representation of Motion and Position Map for Cell Tracking [10.463365653675694]
  We propose the Motion and Position Map (MPM) that jointly represents both detection and association. It guarantees coherence such that if a cell is detected, the corresponding motion flow can always be obtained. It is a simple but powerful method for multi-object tracking in dense environments.
  arXiv  Detail & Related papers  (2020-02-25T09:06:55Z)

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.