@inproceedings {27636,
	title = {Automatic Polyp Segmentation using U-Net-ResNet50},
	journal = {Medico MediaEval 2020},
	year = {2021},
	publisher = {MediaEval},
	abstract = {Polyps are the predecessors to colorectal cancer which is considered as one of the leading causes of cancer-related deaths worldwide. Colonoscopy is the standard procedure for the identification, localization, and removal of colorectal polyps. Due to variability in shape, size, and surrounding tissue similarity, colorectal polyps are often missed by the clinicians during colonoscopy. With the use of an automatic, accurate, and fast polyp segmentation method during the colonoscopy, many colorectal polyps can be easily detected and removed. The {\textquoteleft}{\textquoteleft}Medico automatic polyp segmentation challenge{\textquoteright}{\textquoteright} provides an opportunity to study polyp segmentation and build an efficient and accurate segmentation algorithm. We use the U-Net with pre-trained ResNet50 as the encoder for the polyp segmentation. The model is trained on Kvasir-SEG dataset provided for the challenge and tested on the organizer{\textquoteright}s dataset and achieves a dice coefficient of 0.8154, Jaccard of 0.7396, recall of 0.8533, precision of 0.8532, accuracy of 0.9506, and F2 score of 0.8272, demonstrating the generalization ability of our model.},
	author = {Alam, Saruar and Nikhil Kumar {Tomar} and Thakur, Aarati and Jha, Debesh and Rauniyar, Ashish}
}
@inproceedings {27635,
	title = {DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation},
	journal = {25th International Conference on Pattern Recognition},
	year = {2021},
	pages = {307-314},
	publisher = { Springer},
	abstract = {Colonoscopy is the gold standard for examination and detection of colorectal polyps. Localization and delineation of polyps can play a vital role in treatment (e.g., surgical planning) and prognostic decision making. Polyp segmentation can provide detailed boundary information for clinical analysis. Convolutional neural networks have improved the performance in colonoscopy. However, polyps usually possess various challenges, such as intra-and inter-class variation and noise. While manual labeling for polyp assessment requires time from experts and is prone to human error (e.g., missed lesions), an automated, accurate, and fast segmentation can improve the quality of delineated lesion boundaries and reduce missed rate. The Endotect challenge provides an opportunity to benchmark computer vision methods by training on the publicly available Hyperkvasir and testing on a separate unseen dataset. In this paper, we propose a novel architecture called {\textquoteleft}{\textquoteleft}DDANet{\textquoteright}{\textquoteright} based on a dual decoder attention network. Our experiments demonstrate that the model trained on the Kvasir-SEG dataset and tested on an unseen dataset achieves a dice coefficient of 0.7874, mIoU of 0.7010, recall of 0.7987, and a precision of 0.8577, demonstrating the generalization ability of our model.},
	keywords = {Benchmarking, Convolutional neural network, deep learning, Polyp segmentation},
	author = {Nikhil Kumar {Tomar} and Jha, Debesh and Ali, Sharib and H{\r a}vard D. {Johansen} and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {28020,
	title = {Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy},
	journal = {2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)},
	year = {2021},
	pages = {1-4},
	publisher = {IEEE},
	abstract = {Minimally Invasive Surgery (MIS) is a surgical intervention used to examine the organs inside the abdomen and has been widely used due to its effectiveness over open surgery. Due to the hardware improvements such as high definition cameras, this procedure has significantly improved and new software methods have open potential for computer-assisted procedures. However, there exists challenges and requirement to improve detection and tracking of the position of the instruments during these surgical procedures. To this end, we evaluate and compare some popular deep learning methods that can potentially be explored for the automated segmentation of surgical instruments in laparoscopy, an important step towards tool tracking. Our experimental results demonstrate that the Dual decoder attention network (DDANet) produces a superior result compared to other recent deep learning methods. DDANet produces a dice coefficient of 0.8739 and mean intersection over union of 0.8183 for the Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge 2019 dataset, at a real-time speed of 101.36 frames per second which is critical for such procedures.},
	keywords = {Real-time segmentation, minimally invasive surgery, surgical instruments,  laparoscopy, deep learning},
	author = {Jha, Debesh and Ali, Sharib and Nikhil Kumar {Tomar} and Riegler, Michael and Johansen, Dag and H{\r a}vard D {Johansen} and Halvorsen, P{\r a}l}
}
@inproceedings {27856,
	title = {NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy},
	journal = {34th IEEE CBMS International Symposium on Computer-Based Medical Systems},
	year = {2021},
	publisher = { IEEE},
	abstract = {Deep learning in gastrointestinal endoscopy can assist to improve clinical performance and be helpful to assess lesions more accurately. To this extent, semantic segmentation methods that can perform automated real-time delineation of a region-of-interest, e.g., boundary identification of cancer or precancerous lesions, can benefit both diagnosis and interventions. However, accurate and real-time segmentation of endoscopic images is extremely challenging due to its high operator dependence and high-definition image quality. To utilize automated methods in clinical settings, it is crucial to design lightweight models with low latency such that they can be integrated with low-end endoscope hardware devices. In this work, we propose NanoNet, a novel architecture for the segmentation of video capsule endoscopy and colonoscopy images. Our proposed architecture allows real-time performance and has higher segmentation accuracy compared to other more complex ones. We use video capsule endoscopy and standard colonoscopy datasets with polyps, and a dataset consisting of endoscopy biopsies and surgical instruments, to evaluate the effectiveness of our approach. Our experiments demonstrate the increased performance of our architecture in terms of a trade-off between model complexity, speed, model parameters, and metric performances. Moreover, the resulting model size is relatively tiny, with only nearly 36,000 parameters compared to traditional deep learning approaches having millions of parameters.},
	keywords = {colonoscopy, deep learning, segmentation, tool  segmentation, Video capsule endoscopy},
	author = {Jha, Debesh and Nikhil Kumar {Tomar} and Ali, Sharib and Riegler, Michael and H{\r a}vard D {Johansen} and Johansen, Dag and de Lange, Thomas and Halvorsen, P{\r a}l}
}
@article {27773,
	title = {Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning},
	journal = {IEEE Access},
	volume = {9},
	year = {2021},
	pages = {40496-40510},
	publisher = { IEEE},
	abstract = {Computer-aided detection, localization, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer vision methods that can be applied to polyp datasets. Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks. Furthermore, it ensures that the produced results in the community are reproducible and provide a fair comparison of developed methods. In this paper, we benchmark several recent state-of-the-art methods using Kvasir-SEG, an open-access dataset of colonoscopy images for polyp detection, localization, and segmentation evaluating both method accuracy and speed. Whilst, most methods in literature have competitive performance over accuracy, we show that the proposed ColonSegNet achieved a better trade-off between an average precision of 0.8000 and mean IoU of 0.8100, and the fastest speed of 180 frames per second for the detection and localization task. Likewise, the proposed ColonSegNetachieved a competitive dice coefficient of 0.8206 and the best average speed of 182.38 frames per second for the segmentation task. Our comprehensive comparison with various state-of-the-art methods reveals the importance of benchmarking the deep learning methods for automated real-time polyp identification and delineations that can potentially transform current clinical practices and minimize miss-detection rates.},
	keywords = {Medical image segmentation,  ColonSegNet, colonoscopy, polyps, deep learning, detection, localization, benchmarking, Kvasir-SEG},
	doi = {10.1109/ACCESS.2021.3063716},
	author = {Jha, Debesh and Ali, Sharib and Nikhil Kumar {Tomar} and H{\r a}vard D {Johansen} and Johansen, Dag and Rittscher, Jens and Riegler, Michael and Halvorsen, P{\r a}l}
}
