dc.contributor.author | Srivastava, Abhishek | |
dc.contributor.author | Jha, Debesh | |
dc.contributor.author | Chanda, Sukalpa | |
dc.contributor.author | Pal, Umapada | |
dc.contributor.author | Johansen, Håvard D. | |
dc.contributor.author | Johansen, Dag | |
dc.contributor.author | Riegler, Michael | |
dc.contributor.author | Ali, Sharib | |
dc.contributor.author | Halvorsen, Pål | |
dc.date.accessioned | 2022-02-16T13:48:20Z | |
dc.date.available | 2022-02-16T13:48:20Z | |
dc.date.issued | 2021-12-23 | |
dc.description.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 for 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 problems.
In this paper, we propose a novel architecture called MultiScale Residual Fusion Network (MSRF-Net), which is specially designed for medical image segmentation. The proposed MSRF-Net is able to exchange multi-scale features
of varying receptive fields using a Dual-Scale Dense Fusion
(DSDF) block. 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 the cutting-edge medical image segmentation methods on four publicly available datasets. We achieve the
Dice Coefficient (DSC) of 0.9217, 0.9420, and 0.9224, 0.8824
on Kvasir-SEG, CVC-ClinicDB, 2018 Data Science Bowl
dataset, and ISIC-2018 skin lesion segmentation challenge
dataset respectively. We further conducted generalizability
tests and achieved DSC of 0.7921 and 0.7575 on CVCClinicDB and Kvasir-SEG, respectively. | en_US |
dc.identifier.citation | Srivastava A, Jha D, Chanda S, Pal U, Johansen HJ, Johansen D, Riegler M, Ali S, Halvorsen P. MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation. IEEE journal of biomedical and health informatics. 2021 | en_US |
dc.identifier.cristinID | FRIDAID 1971848 | |
dc.identifier.doi | 10.1109/JBHI.2021.3138024 | |
dc.identifier.issn | 2168-2194 | |
dc.identifier.issn | 2168-2208 | |
dc.identifier.uri | https://hdl.handle.net/10037/24072 | |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.relation.journal | IEEE journal of biomedical and health informatics | |
dc.rights.accessRights | openAccess | en_US |
dc.rights.holder | Copyright 2021 The Author(s) | en_US |
dc.title | MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation | en_US |
dc.type.version | publishedVersion | en_US |
dc.type | Journal article | en_US |
dc.type | Tidsskriftartikkel | en_US |
dc.type | Peer reviewed | en_US |