Two-Stream UNET Networks for Semantic Segmentation in Medical Images
- URL: http://arxiv.org/abs/2207.13337v1
- Date: Wed, 27 Jul 2022 07:45:11 GMT
- Title: Two-Stream UNET Networks for Semantic Segmentation in Medical Images
- Authors: Xin Chen, Ke Ding
- Abstract summary: We propose a novel two-stream UNET architecture for automatic end-to-end medical image segmentation.
We demonstrate that two-stream CNNs with more low-level features greatly benefit semantic segmentation for imperfect medical image datasets.
- Score: 4.1462578830708345
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advances of semantic image segmentation greatly benefit from deeper
and larger Convolutional Neural Network (CNN) models. Compared to image
segmentation in the wild, properties of both medical images themselves and of
existing medical datasets hinder training deeper and larger models because of
overfitting. To this end, we propose a novel two-stream UNET architecture for
automatic end-to-end medical image segmentation, in which intensity value and
gradient vector flow (GVF) are two inputs for each stream, respectively. We
demonstrate that two-stream CNNs with more low-level features greatly benefit
semantic segmentation for imperfect medical image datasets. Our proposed
two-stream networks are trained and evaluated on the popular medical image
segmentation benchmarks, and the results are competitive with the state of the
art. The code will be released soon.
Related papers
- I-MedSAM: Implicit Medical Image Segmentation with Segment Anything [24.04558900909617]
We propose I-MedSAM, which leverages the benefits of both continuous representations and SAM to obtain better cross-domain ability and accurate boundary delineation.
Our proposed method with only 1.6M trainable parameters outperforms existing methods including discrete and implicit methods.
arXiv Detail & Related papers (2023-11-28T00:43:52Z) - Scale-aware Super-resolution Network with Dual Affinity Learning for
Lesion Segmentation from Medical Images [50.76668288066681]
We present a scale-aware super-resolution network to adaptively segment lesions of various sizes from low-resolution medical images.
Our proposed network achieved consistent improvements compared to other state-of-the-art methods.
arXiv Detail & Related papers (2023-05-30T14:25:55Z) - M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical
Image Segmentation [73.10707675345253]
We propose a general multi-scale in multi-scale subtraction network (M$2$SNet) to finish diverse segmentation from medical image.
Our method performs favorably against most state-of-the-art methods under different evaluation metrics on eleven datasets of four different medical image segmentation tasks.
arXiv Detail & Related papers (2023-03-20T06:26:49Z) - MedSegDiff-V2: Diffusion based Medical Image Segmentation with
Transformer [53.575573940055335]
We propose a novel Transformer-based Diffusion framework, called MedSegDiff-V2.
We verify its effectiveness on 20 medical image segmentation tasks with different image modalities.
arXiv Detail & Related papers (2023-01-19T03:42:36Z) - Self-Supervised Correction Learning for Semi-Supervised Biomedical Image
Segmentation [84.58210297703714]
We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation.
We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting.
Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
arXiv Detail & Related papers (2023-01-12T08:19:46Z) - Recurrent Mask Refinement for Few-Shot Medical Image Segmentation [15.775057485500348]
We propose a new framework for few-shot medical image segmentation based on prototypical networks.
Our innovation lies in the design of two key modules: 1) a context relation encoder (CRE) that uses correlation to capture local relation features between foreground and background regions.
Experiments on two abdomen CT datasets and an abdomen MRI dataset show the proposed method obtains substantial improvement over the state-of-the-art methods.
arXiv Detail & Related papers (2021-08-02T04:06:12Z) - Learning With Context Feedback Loop for Robust Medical Image
Segmentation [1.881091632124107]
We present a fully automatic deep learning method for medical image segmentation using two systems.
The first one is a forward system of an encoder-decoder CNN that predicts the segmentation result from the input image.
The predicted probabilistic output of the forward system is then encoded by a fully convolutional network (FCN)-based context feedback system.
arXiv Detail & Related papers (2021-03-04T05:44:59Z) - 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) - DONet: Dual Objective Networks for Skin Lesion Segmentation [77.9806410198298]
We propose a simple yet effective framework, named Dual Objective Networks (DONet), to improve the skin lesion segmentation.
Our DONet adopts two symmetric decoders to produce different predictions for approaching different objectives.
To address the challenge of large variety of lesion scales and shapes in dermoscopic images, we additionally propose a recurrent context encoding module (RCEM)
arXiv Detail & Related papers (2020-08-19T06:02:46Z) - DoubleU-Net: A Deep Convolutional Neural Network for Medical Image
Segmentation [1.6416058750198184]
DoubleU-Net is a combination of two U-Net architectures stacked on top of each other.
We have evaluated DoubleU-Net using four medical segmentation datasets.
arXiv Detail & Related papers (2020-06-08T18:38:24Z) - Boundary-aware Context Neural Network for Medical Image Segmentation [15.585851505721433]
Medical image segmentation can provide reliable basis for further clinical analysis and disease diagnosis.
Most existing CNNs-based methods produce unsatisfactory segmentation mask without accurate object boundaries.
In this paper, we formulate a boundary-aware context neural network (BA-Net) for 2D medical image segmentation.
arXiv Detail & Related papers (2020-05-03T02:35:49Z)
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.