NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy

Jha, Debesh; Tomar, Nikhil Kumar; Ali, Sharib; Riegler, Michael; Johansen, Håvard D.; Johansen, Dag; Lange, Thomas de; Halvorsen, Pål

doi:10.48550/arxiv.2104.11138

Cited by 4 publications

(9 citation statements)

References 29 publications

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“…This work is very similar to the works of [15,18], but it uses other segmentation models that are gaining prominence in the literature with other medical segmentation datasets [19][20][21][22].…”

Section: Related Workmentioning

confidence: 95%

“…The Nano-Net model was proposed in the paper by [22], and its objective is to perform accurate segmentation in real time. As a result, Nano-Net uses the encoder-decoder model (see Figure 4), but in the encoder a pre-trained model is used so that the convergence of the model is faster.…”

Section: Nano-netmentioning

confidence: 99%

“…The decoder, in turn, is formed by three blocks. In the paper by [22], three configurations of Nano-Net are proposed (Nano-Net-A, Nano-Net-B and Nano-Net-C); the difference between the models is the number of feature channels used, where A is 32, 64 and 128; B is 32, 64 and 96; and C is 16, 24 and 32. In the present paper, Nano-Net-A was used because it was the model that obtained the best results.…”

Section: Nano-netmentioning

confidence: 99%

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A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph

Rocha

Endo

2022

Applied Sciences

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Introduction: Dental segmentation in panoramic radiograph has become very relevant in dentistry, since it allows health professionals to carry out their assessments more clearly and helps them to define the best possible treatment plan for their patients. Objectives: In this work, a comparative study is carried out with four segmentation algorithms (U-Net, DCU-Net, DoubleU-Net and Nano-Net) that are prominent in the medical literature on segmentation and we evaluate their results with the current state of the art of dental segmentation in panoramic radiograph. Methods: These algorithms were tested with a dataset consisting of 1500 images, considering experiment scenarios with and without augmentation data. Results: DoubleU-Net was the model that presented the best results among the analyzed models, reaching 96.591% accuracy and 92.886% Dice using the dataset with data augmentation. Another model that stood out was Nano-Net using the dataset without data augmentation; this model achieved results close to that of the literature with only 235 thousand trainable parameters, while the literature model (TSASNet) contains 78 million. Conclusions: The results obtained in this work are satisfactory and present paths for a better and more effective dental segmentation process.

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Section: Related Workmentioning

confidence: 95%

Section: Nano-netmentioning

confidence: 99%

Section: Nano-netmentioning

confidence: 99%

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A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph

Rocha

Endo

2022

Applied Sciences

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“…There are some approaches for detecting the abnormalities and the landmarks with 23 classes in total, including various severity levels of the abnormalities. The approaches of the DDANet ( Tomar et al, 2021 ), NanoNet ( Jha et al, 2021b ), and Data Bagging ( Khan et al, 2021 ) resulted in good detection of F1-score of 0.83, 0.72 and 0.60, respectively. Some of the approaches are performing best in the case of real-time detection and classification.…”

Section: Related Workmentioning

confidence: 99%

Real time anatomical landmarks and abnormalities detection in gastrointestinal tract

Khan,

Tahir

2023

PeerJ Computer Science

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Gastrointestinal (GI) endoscopy is an active research field due to the lethal cancer diseases in the GI tract. Cancer treatments result better if diagnosed early and it increases the survival chances. There is a high miss rate in the detection of the abnormalities in the GI tract during endoscopy or colonoscopy due to the lack of attentiveness, tiring procedures, or the lack of required training. The procedure of the detection can be automated to the reduction of the risks by identifying and flagging the suspicious frames. A suspicious frame may have some of the abnormality or the information about anatomical landmark in the frame. The frame then can be analysed for the anatomical landmarks and the abnormalities for the detection of disease. In this research, a real-time endoscopic abnormalities detection system is presented that detects the abnormalities and the landmarks. The proposed system is based on a combination of handcrafted and deep features. Deep features are extracted from lightweight MobileNet convolutional neural network (CNN) architecture. There are some of the classes with a small inter-class difference and a higher intra-class differences, for such classes the same detection threshold is unable to distinguish. The threshold of such classes is learned from the training data using genetic algorithm. The system is evaluated on various benchmark datasets and resulted in an accuracy of 0.99 with the F1-score of 0.91 and Matthews correlation coefficient (MCC) of 0.91 on Kvasir datasets and F1-score of 0.93 on the dataset of DowPK. The system detects abnormalities in real-time with the detection speed of 41 frames per second.

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“…Early identification of polyps in GI tract is critical to prevent colorectal cancers [13]. Therefore, many ML models have been investigated to segment polyps automatically in GI tract videos recorded from endoscopy [14,15,16] or PilCams examinations [17,18,19] to augment performance of doctors by detecting polyps missed by experts, thereby both decreasing the miss rates and reducing the observer variations.…”

Section: Introductionmentioning

confidence: 99%

SinGAN-Seg: Synthetic training data generation for medical image segmentation

Thambawita,

Salehi,

Sheshkal

et al. 2021

Preprint

Self Cite

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Processing medical data to find abnormalities is a time-consuming and costly task, requiring tremendous efforts from medical experts. Therefore, artificial intelligence (AI) has become a popular tool for the automatic processing of medical data, acting as a supportive tool for doctors. AI tools highly depend on data for training the models. However, there are several constraints to access to large amounts of medical data to train machine learning algorithms in the medical domain, e.g., due to privacy concerns and the costly, time-consuming medical data annotation process.To address this, in this paper we present a novel synthetic data generation pipeline called SinGAN-Seg to produce synthetic medical data with the corresponding annotated ground truth masks. We show that these synthetic data generation pipelines can be used as an alternative to bypass privacy concerns and as an alternative way to produce artificial segmentation datasets with corresponding ground truth masks to avoid the tedious medical data annotation process. As a proof of concept, we used an open polyp segmentation dataset. By training UNet++ using both real polyp segmentation dataset and the corresponding synthetic dataset generated from the SinGAN-Seg pipeline, we show that the synthetic data can achieve a very close per-formance to the real data when the real segmentation datasets are large enough. In addition, we show that synthetic data generated from the SinGAN-Seg pipeline improving the performance of segmentation algorithms when the training dataset is very small. Since our SinGAN-Seg pipeline is applicable for any medical dataset, this pipeline can be used with any other segmentation datasets.

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NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy

Cited by 4 publications

References 29 publications

A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph

A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph

Real time anatomical landmarks and abnormalities detection in gastrointestinal tract

SinGAN-Seg: Synthetic training data generation for medical image segmentation

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