Towards Automated Semantic Segmentation in Mammography Images
- URL: http://arxiv.org/abs/2307.10296v1
- Date: Tue, 18 Jul 2023 15:04:42 GMT
- Title: Towards Automated Semantic Segmentation in Mammography Images
- Authors: Cesar A. Sierra-Franco, Jan Hurtado, Victor de A. Thomaz, Leonardo C.
da Cruz, Santiago V. Silva, and Alberto B. Raposo
- Abstract summary: We propose a deep learning-based framework for the segmentation of the nipple, the pectoral muscle, the fibroglandular tissue, and the fatty tissue on standard-view mammography images.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Mammography images are widely used to detect non-palpable breast lesions or
nodules, preventing cancer and providing the opportunity to plan interventions
when necessary. The identification of some structures of interest is essential
to make a diagnosis and evaluate image adequacy. Thus, computer-aided detection
systems can be helpful in assisting medical interpretation by automatically
segmenting these landmark structures. In this paper, we propose a deep
learning-based framework for the segmentation of the nipple, the pectoral
muscle, the fibroglandular tissue, and the fatty tissue on standard-view
mammography images. We introduce a large private segmentation dataset and
extensive experiments considering different deep-learning model architectures.
Our experiments demonstrate accurate segmentation performance on variate and
challenging cases, showing that this framework can be integrated into clinical
practice.
Related papers
- A novel approach towards the classification of Bone Fracture from Musculoskeletal Radiography images using Attention Based Transfer Learning [0.0]
We deploy an attention-based transfer learning model to detect bone fractures in X-ray scans.
Our model achieves a state-of-the-art accuracy of more than 90% in fracture classification.
arXiv Detail & Related papers (2024-10-18T19:07:24Z) - Scribble-Based Interactive Segmentation of Medical Hyperspectral Images [4.675955891956077]
This work introduces a scribble-based interactive segmentation framework for medical hyperspectral images.
The proposed method utilizes deep learning for feature extraction and a geodesic distance map generated from user-provided scribbles.
arXiv Detail & Related papers (2024-08-05T12:33:07Z) - Advancing Medical Image Segmentation: Morphology-Driven Learning with Diffusion Transformer [4.672688418357066]
We propose a novel Transformer Diffusion (DTS) model for robust segmentation in the presence of noise.
Our model, which analyzes the morphological representation of images, shows better results than the previous models in various medical imaging modalities.
arXiv Detail & Related papers (2024-08-01T07:35:54Z) - Region-based Contrastive Pretraining for Medical Image Retrieval with
Anatomic Query [56.54255735943497]
Region-based contrastive pretraining for Medical Image Retrieval (RegionMIR)
We introduce a novel Region-based contrastive pretraining for Medical Image Retrieval (RegionMIR)
arXiv Detail & Related papers (2023-05-09T16:46:33Z) - Human Treelike Tubular Structure Segmentation: A Comprehensive Review
and Future Perspectives [8.103169967374944]
structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales.
Large collections of 2D and 3D images have been made available by medical imaging modalities.
Analysis of structure provides insights into disease diagnosis, treatment planning, and prognosis.
arXiv Detail & Related papers (2022-07-12T17:01:42Z) - SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection [76.01333073259677]
We propose the use of Space-aware Memory Queues for In-painting and Detecting anomalies from radiography images (abbreviated as SQUID)
We show that SQUID can taxonomize the ingrained anatomical structures into recurrent patterns; and in the inference, it can identify anomalies (unseen/modified patterns) in the image.
arXiv Detail & Related papers (2021-11-26T13:47:34Z) - Semantic segmentation of multispectral photoacoustic images using deep
learning [53.65837038435433]
Photoacoustic imaging has the potential to revolutionise healthcare.
Clinical translation of the technology requires conversion of the high-dimensional acquired data into clinically relevant and interpretable information.
We present a deep learning-based approach to semantic segmentation of multispectral photoacoustic images.
arXiv Detail & Related papers (2021-05-20T09:33:55Z) - Few-shot Medical Image Segmentation using a Global Correlation Network
with Discriminative Embedding [60.89561661441736]
We propose a novel method for few-shot medical image segmentation.
We construct our few-shot image segmentor using a deep convolutional network trained episodically.
We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
arXiv Detail & Related papers (2020-12-10T04:01:07Z) - Retinal Image Segmentation with a Structure-Texture Demixing Network [62.69128827622726]
The complex structure and texture information are mixed in a retinal image, and distinguishing the information is difficult.
Existing methods handle texture and structure jointly, which may lead biased models toward recognizing textures and thus results in inferior segmentation performance.
We propose a segmentation strategy that seeks to separate structure and texture components and significantly improve the performance.
arXiv Detail & Related papers (2020-07-15T12:19:03Z) - Weakly supervised multiple instance learning histopathological tumor
segmentation [51.085268272912415]
We propose a weakly supervised framework for whole slide imaging segmentation.
We exploit a multiple instance learning scheme for training models.
The proposed framework has been evaluated on multi-locations and multi-centric public data from The Cancer Genome Atlas and the PatchCamelyon dataset.
arXiv Detail & Related papers (2020-04-10T13:12:47Z)
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.