AxonCallosumEM Dataset: Axon Semantic Segmentation of Whole Corpus Callosum cross section from EM Images

• URL: http://arxiv.org/abs/2307.02464v1
• Date: Wed, 5 Jul 2023 17:38:01 GMT
• Title: AxonCallosumEM Dataset: Axon Semantic Segmentation of Whole Corpus Callosum cross section from EM Images
• Authors: Ao Cheng and Guoqiang Zhao and Lirong Wang and Ruobing Zhang
• Abstract summary: AxonCallosumEM dataset comprises a 1.83 times 5.76mm EM image captured from the corpus callosum of the Rett Syndrome (RTT) mouse model. We meticulously proofread over 600,000 patches at a resolution of 1024 times 1024, thus providing a comprehensive ground truth for myelinated axons and myelin sheaths.
• Score: 1.433758865948252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The electron microscope (EM) remains the predominant technique for elucidating intricate details of the animal nervous system at the nanometer scale. However, accurately reconstructing the complex morphology of axons and myelin sheaths poses a significant challenge. Furthermore, the absence of publicly available, large-scale EM datasets encompassing complete cross sections of the corpus callosum, with dense ground truth segmentation for axons and myelin sheaths, hinders the advancement and evaluation of holistic corpus callosum reconstructions. To surmount these obstacles, we introduce the AxonCallosumEM dataset, comprising a 1.83 times 5.76mm EM image captured from the corpus callosum of the Rett Syndrome (RTT) mouse model, which entail extensive axon bundles. We meticulously proofread over 600,000 patches at a resolution of 1024 times 1024, thus providing a comprehensive ground truth for myelinated axons and myelin sheaths. Additionally, we extensively annotated three distinct regions within the dataset for the purposes of training, testing, and validation. Utilizing this dataset, we develop a fine-tuning methodology that adapts Segment Anything Model (SAM) to EM images segmentation tasks, called EM-SAM, enabling outperforms other state-of-the-art methods. Furthermore, we present the evaluation results of EM-SAM as a baseline.

Related papers

• ShapeMamba-EM: Fine-Tuning Foundation Model with Local Shape Descriptors and Mamba Blocks for 3D EM Image Segmentation [49.42525661521625]
  This paper presents ShapeMamba-EM, a specialized fine-tuning method for 3D EM segmentation. It is tested over a wide range of EM images, covering five segmentation tasks and 10 datasets.
  arXiv  Detail & Related papers  (2024-08-26T08:59:22Z)
• High-resolution segmentations of the hypothalamus and its subregions for training of segmentation models [1.0486773259892048]
  HELM, Hypothalamic ex vivo Label Maps is a dataset composed of label maps built from publicly available ultra-high resolution ex vivo MRI from 10 whole hemispheres. We provide a combination of manual labels for the hypothalamic regions and automated segmentations for the rest of the brain, and mirrored to simulate entire brains.
  arXiv  Detail & Related papers  (2024-06-27T19:16:57Z)
• Meply: A Large-scale Dataset and Baseline Evaluations for Metastatic Perirectal Lymph Node Detection and Segmentation [10.250943622693429]
  We present the first large-scale perirectal metastatic lymph node CT image dataset called Meply. We introduce a novel lymph-node segmentation model named CoSAM. The CoSAM utilizes sequence-based detection to guide the segmentation of metastatic lymph nodes in rectal cancer.
  arXiv  Detail & Related papers  (2024-04-13T07:30:16Z)
• Mask-Enhanced Segment Anything Model for Tumor Lesion Semantic Segmentation [48.107348956719775]
  We introduce Mask-Enhanced SAM (M-SAM), an innovative architecture tailored for 3D tumor lesion segmentation. We propose a novel Mask-Enhanced Adapter (MEA) within M-SAM that enriches the semantic information of medical images with positional data from coarse segmentation masks. Our M-SAM achieves high segmentation accuracy and also exhibits robust generalization.
  arXiv  Detail & Related papers  (2024-03-09T13:37:02Z)
• MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation [58.53672866662472]
  We introduce a modality-agnostic SAM adaptation framework, named as MA-SAM. Our method roots in the parameter-efficient fine-tuning strategy to update only a small portion of weight increments. By injecting a series of 3D adapters into the transformer blocks of the image encoder, our method enables the pre-trained 2D backbone to extract third-dimensional information from input data.
  arXiv  Detail & Related papers  (2023-09-16T02:41:53Z)
• The XPRESS Challenge: Xray Projectomic Reconstruction -- Extracting Segmentation with Skeletons [65.73888157730973]
  X-ray holographic nanotomography (XNH) can provide high-resolution images of brain tissue at a much larger scale than microscopy. We provide XNH images of cortical white matter axons from the mouse brain along with ground truth annotations for axon trajectories.
  arXiv  Detail & Related papers  (2023-02-08T00:53:46Z)
• Semi-Supervised Segmentation of Mitochondria from Electron Microscopy Images Using Spatial Continuity [3.631638087834872]
  We propose a semi-supervised deep learning model that segments mitochondria by leveraging the spatial continuity of their structural, morphological, and contextual information. Our model achieves performance similar to that of state-of-the-art fully supervised models but requires only 20% of their annotated training data.
  arXiv  Detail & Related papers  (2022-06-06T06:52:19Z)
• gACSON software for automated segmentation and morphology analyses of myelinated axons in 3D electron microscopy [55.78588835407174]
  We introduce a freely available gACSON software for visualization, segmentation, assessment, and morphology analysis of myelinated axons in 3D-EM volumes. gACSON automatically segments the intra-axonal space of myelinated axons and their corresponding myelin sheaths. It analyzes the morphology of myelinated axons, such as axonal diameter, axonal eccentricity, myelin thickness, or g-ratio.
  arXiv  Detail & Related papers  (2021-12-13T08:17:15Z)
• AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions [22.87130178598667]
  AxonEM dataset consists of two 30x30x30 um3 EM image volumes from the human and mouse cortex. We thoroughly proofread over 18,000 axon instances to provide dense 3D axon instance segmentation. We densely annotate nine ground truth subvolumes for training, per each data volume.
  arXiv  Detail & Related papers  (2021-07-12T14:24:03Z)
• Deep Negative Volume Segmentation [60.44793799306154]
  We propose a new angle to the 3D segmentation task: segment empty spaces between all the tissues surrounding the object. Our approach is an end-to-end pipeline that comprises a V-Net for bone segmentation. We validate the idea on the CT scans in a 50-patient dataset, annotated by experts in maxillofacial medicine.
  arXiv  Detail & Related papers  (2020-06-22T16:55:23Z)

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.