MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image
Segmentation
- URL: http://arxiv.org/abs/2105.07451v1
- Date: Sun, 16 May 2021 15:19:56 GMT
- Title: MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image
Segmentation
- Authors: Abhishek Srivastava, Debesh Jha, Sukalpa Chanda, Umapada Pal,
H{\aa}vard D. Johansen, Dag Johansen, Michael A. Riegler, Sharib Ali, P{\aa}l
Halvorsen
- Abstract summary: We propose a novel architecture called MSRF-Net, which is specially designed for medical image segmentation tasks.
MSRF-Net is able to exchange multi-scale features of varying receptive fields using a dual-scale dense fusion block (DSDF)
Our DSDF block can exchange information rigorously across two different resolution scales, and our MSRF sub-network uses multiple DSDF blocks in sequence to perform multi-scale fusion.
- Score: 10.979393806308648
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Methods based on convolutional neural networks have improved the performance
of biomedical image segmentation. However, most of these methods cannot
efficiently segment objects of variable sizes and train on small and biased
datasets, which are common in biomedical use cases. While methods exist that
incorporate multi-scale fusion approaches to address the challenges arising
with variable sizes, they usually use complex models that are more suitable for
general semantic segmentation computer vision problems. In this paper, we
propose a novel architecture called MSRF-Net, which is specially designed for
medical image segmentation tasks. The proposed MSRF-Net is able to exchange
multi-scale features of varying receptive fields using a dual-scale dense
fusion block (DSDF). Our DSDF block can exchange information rigorously across
two different resolution scales, and our MSRF sub-network uses multiple DSDF
blocks in sequence to perform multi-scale fusion. This allows the preservation
of resolution, improved information flow, and propagation of both high- and
low-level features to obtain accurate segmentation maps. The proposed MSRF-Net
allows to capture object variabilities and provides improved results on
different biomedical datasets. Extensive experiments on MSRF-Net demonstrate
that the proposed method outperforms most of the cutting-edge medical image
segmentation state-of-the-art methods. MSRF-Net advances the performance on
four publicly available datasets, and also, MSRF-Net is more generalizable as
compared to state-of-the-art methods.
Related papers
- TransResNet: Integrating the Strengths of ViTs and CNNs for High Resolution Medical Image Segmentation via Feature Grafting [6.987177704136503]
High-resolution images are preferable in medical imaging domain as they significantly improve the diagnostic capability of the underlying method.
Most of the existing deep learning-based techniques for medical image segmentation are optimized for input images having small spatial dimensions and perform poorly on high-resolution images.
We propose a parallel-in-branch architecture called TransResNet, which incorporates Transformer and CNN in a parallel manner to extract features from multi-resolution images independently.
arXiv Detail & Related papers (2024-10-01T18:22:34Z) - Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention [1.1155836879100416]
We propose a Modality-agnostic Domain Generalizable Network (MADGNet) for medical image segmentation.
MFMSA block refines the process of spatial feature extraction, particularly in capturing boundary features.
E-SDM mitigates information loss in multi-task learning with deep supervision.
arXiv Detail & Related papers (2024-05-10T07:34:36Z) - MFA-Net: Multi-Scale feature fusion attention network for liver tumor segmentation [36.837642256513426]
We propose a new segmentation framework based on attention mechanisms, named MFA-Net.
The proposed framework can learn more meaningful feature maps among multiple scales and result in more accurate automatic segmentation.
arXiv Detail & Related papers (2024-05-07T07:10:44Z) - SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion
Classification Using 3D Multi-Phase Imaging [59.78761085714715]
This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework for liver lesion classification.
The proposed framework has been validated through comprehensive experiments on two clinical datasets.
To support the scientific community, we are releasing our extensive multi-phase MR dataset for liver lesion analysis to the public.
arXiv Detail & Related papers (2024-02-27T06:32:56Z) - Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
Medical image segmentation plays a crucial role in computer-aided diagnosis.
We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
arXiv Detail & Related papers (2023-12-26T12:56:31Z) - Interpretable Small Training Set Image Segmentation Network Originated
from Multi-Grid Variational Model [5.283735137946097]
Deep learning (DL) methods have been proposed and widely used for image segmentation.
DL methods usually require a large amount of manually segmented data as training data and suffer from poor interpretability.
In this paper, we replace the hand-crafted regularity term in the MS model with a data adaptive generalized learnable regularity term.
arXiv Detail & Related papers (2023-06-25T02:34:34Z) - 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) - AF$_2$: Adaptive Focus Framework for Aerial Imagery Segmentation [86.44683367028914]
Aerial imagery segmentation has some unique challenges, the most critical one among which lies in foreground-background imbalance.
We propose Adaptive Focus Framework (AF$), which adopts a hierarchical segmentation procedure and focuses on adaptively utilizing multi-scale representations.
AF$ has significantly improved the accuracy on three widely used aerial benchmarks, as fast as the mainstream method.
arXiv Detail & Related papers (2022-02-18T10:14:45Z) - Cross-Modality Brain Tumor Segmentation via Bidirectional
Global-to-Local Unsupervised Domain Adaptation [61.01704175938995]
In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA scheme.
Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor.
The proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin.
arXiv Detail & Related papers (2021-05-17T10:11:45Z) - Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention
and Dynamic Resampling [13.542898009730804]
The performance of relevant algorithms is significantly affected by the proper fusion of the multi-modal information.
We present the Max-Fusion U-Net that achieves improved pathology segmentation performance.
We evaluate our methods using the Myocardial pathology segmentation (MyoPS) combining the multi-sequence CMR dataset.
arXiv Detail & Related papers (2020-09-05T17:24:23Z) - MS-Net: Multi-Site Network for Improving Prostate Segmentation with
Heterogeneous MRI Data [75.73881040581767]
We propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations.
Our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.
arXiv Detail & Related papers (2020-02-09T14:11:50Z)
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.