Nested Dilation Network (NDN) for Multi-Task Medical Image Segmentation

Wang, Liansheng; Chen, Rongzhen; Wang, Shuxin; Zeng, Nianyin; Huang, Xiaoyang; Liu, Changhua

doi:10.1109/access.2019.2908386

Cited by 20 publications

(19 citation statements)

References 39 publications

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“…To verify the effectiveness of our proposed improvements with the state of the art methods. We compare our method with two proposed methods by Wang L. et al [48] and Isensee F.et al [49].For the brain and hippocampus dataset, the result is from the papers. For the heart dataset, Wang L.et al do not perform implementation with the heart dataset.…”

Section: G Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Dense Dilated Inception Network for Medical Image Segmentation

Bala¹,

Kant²

2020

IJACSA

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Section: G Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Dense Dilated Inception Network for Medical Image Segmentation

Bala¹,

Kant²

2020

IJACSA

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“…Residual connections facilitate the training process by directly routing the input information to the output and preserves the nobility of the gradient flow. The residual function simplifies the objective of optimization without any additional parameters and boosts the performance, which is the inspiration behind the deeper residual-based network [42]. Equation ( 1) below shows the working principle.…”

Section: A Residual Blocksmentioning

confidence: 99%

A Comprehensive Study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation

Jha

Smedsrud

Johansen

et al. 2021

IEEE J. Biomed. Health Inform.

196

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Colonoscopy is considered the gold standard for detection of colorectal cancer and its precursors. Existing examination methods are, however, hampered by high overall missrate, and many abnormalities are left undetected. Computer-Aided Diagnosis systems based on advanced machine learning algorithms are touted as a game-changer that can identify regions in the colon overlooked by the physicians during endoscopic examinations, and help detect and characterize lesions. In previous work, we have proposed the ResUNet++ architecture and demonstrated that it produces more efficient results compared with its counterparts U-Net and ResUNet. In this paper, we demonstrate that further improvements to the overall prediction performance of the ResUNet++ architecture can be achieved by using Conditional Random Field (CRF) and Test-Time Augmentation (TTA). We have performed extensive evaluations and validated the improvements using six publicly available datasets: Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, ETIS-Larib Polyp DB, ASU-Mayo Clinic Colonoscopy Video Database, and CVC-VideoClinicDB. Moreover, we compare our proposed architecture and resulting model with other State-of-the-art methods. To explore the generalization capability of ResUNet++ on different publicly available polyp datasets, so that it could be used in a real-world setting, we performed an extensive cross-dataset evaluation. The experimental results show that applying CRF and TTA improves the performance on various polyp segmentation datasets both on the same dataset and cross-dataset. To check the model's performance on difficult to detect polyps, we selected, with the help of an expert gastroenterologist, 196 sessile or flat polyps that are less than ten millimeters in size. This additional data has been made available as a subset of Kvasir-SEG. Our approaches showed good results for flat or sessile and smaller polyps, which are known to be one of the major reasons for high polyp miss-rates. This is one of the significant strengths of our work and indicates that our methods should be investigated further for use in clinical practice.

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“…Though 59 used a mesh of attention blocks and residual block as a decoder, both methods tested there model on CVC-ClinicDB achieving F1-score of 96.106 and 96.043, respectively. Furthermore, nested dilation network (NDN) 60 was designed to segment lesions and tested on the GIANA2018 dataset achieving improvements on Dice upto 3% compared to other methods.…”

Section: /26mentioning

confidence: 99%

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

Ali¹,

Ghatwary²,

Jha³

et al. 2022

Preprint

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Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and characterisation. Moreover, colonoscopic surveillance and removal of polyps (referred to as polypectomy ) are highly operator-dependent procedures. There exist a high missed detection rate and incomplete removal of colonic polyps due to their variable nature, the difficulties to delineate the abnormality, the high recurrence rates, and the anatomical topography of the colon. There have been several developments in realising automated methods for both detection and segmentation of these polyps using machine learning. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets that come from different centres, modalities and acquisition systems. To test this hypothesis rigorously we curated a multi-centre and multi-population dataset acquired from multiple colonoscopy systems and challenged teams comprising machine learning experts to develop robust automated detection and segmentation methods as part of our crowd-sourcing Endoscopic computer vision challenge (EndoCV) 2021. In this paper, we analyse the detection results of the four top (among seven) teams and the segmentation results of the five top teams (among 16). Our analyses demonstrate that the top-ranking teams concentrated on accuracy (i.e., accuracy > 80% on overall Dice score on different validation sets) over real-time performance required for clinical applicability. We further dissect the methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets.Author contributions S. Ali conceptualised the work, led the challenge and workshop, prepared the dataset, software and performed all analyses. S. Ali, N. Ghatwary and D. Jha contributed in data annotations. T. de Lange, J.E. East, S. Realdon, R. Cannizzaro, D. Lamarque were involved providing colonoscopy data and in the validation and quality checks of the annotations used in this challenge. Challenge participants (E

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Nested Dilation Network (NDN) for Multi-Task Medical Image Segmentation

Cited by 20 publications

References 39 publications

Dense Dilated Inception Network for Medical Image Segmentation

Dense Dilated Inception Network for Medical Image Segmentation

A Comprehensive Study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

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