Kvasir-Instrument: Diagnostic and Therapeutic Tool Segmentation Dataset in Gastrointestinal Endoscopy

Jha, Debesh; Ali, Sharib; Emanuelsen, Krister; Hicks, Steven A.; Thambawita, Vajira; Garcia-Ceja, Enrique; Riegler, Michael; Lange, Thomas de; Schmidt, Peter T.; Johansen, Håvard D.; Halvorsen, Pål

doi:10.1007/978-3-030-67835-7_19

Cited by 53 publications

(46 citation statements)

References 11 publications

(13 reference statements)

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“…To demonstrate the performance of the proposed model, we employed two different datasets, i.e., Kvasir-Seg [5] and Kvasir-Instrument [6], for polyp segmentation and instrument segmentation, respectively [7]. Both datasets were provided in the MedAI competition as training datasets [7].…”

Section: Resultsmentioning

confidence: 99%

Employing GRU to combine feature maps in DeeplabV3 for a better segmentation model

Haithami

Ahmed

Liao

et al. 2021

NMI

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In this paper, we aim to enhance the segmentation capabilities of DeeplabV3 by employing Gated Recurrent Neural Network (GRU). A 1-by-1 convolution in DeeplabV3 was replaced by GRU after the Atrous Spatial Pyramid Pooling (ASSP) layer to combine the input feature maps. The convolution and GRU have sharable parameters, though, the latter has gates that enable/disable the contribution of each input feature map. The experiments on unseen test sets demonstrate that employing GRU instead of convolution would produce better segmentation results. The used datasets are public datasets provided by MedAI competition.

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Section: Resultsmentioning

confidence: 99%

Employing GRU to combine feature maps in DeeplabV3 for a better segmentation model

Haithami

Ahmed

Liao

et al. 2021

NMI

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“…Table 1 shows final performance on the hidden test set, as provided by the organizers, for the two tasks of Polyp and surgical instrument segmentation after training on the Kvasir-SEG [13] and Kvasir-Instrument [14] datasets.…”

Section: Resultsmentioning

confidence: 99%

Polyp and Surgical Instrument Segmentation with Double Encoder-Decoder Networks

Galdrán

2021

NMI

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This paper describes a solution for the MedAI competition, in which participants were required to segment both polyps and surgical instruments from endoscopic images. Our approach relies on a double encoder-decoder neural network which we have previously applied for polyp segmentation, but with a series of enhancements: a more powerful encoder architecture, an improved optimization procedure, and the post-processing of segmentations, based on tempered model ensembling. Experimental results show that our method produces segmentations that show a good agreement with manual delineations provided by medical experts.

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“…We perform a five-fold cross validation experiments with the Kvasir-SEG [10] and Kvasir-Instruments [11] dataset.…”

Section: Resultsmentioning

confidence: 99%

Dual Parallel Reverse Attention Edge Network : DPRA-EdgeNet

Bhattacharya

Betz

Eggert

et al. 2021

NMI

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In this paper, we propose Dual Parallel Reverse Attention Edge Network (DPRA-EdgeNet), an architecture that jointly learns to segment an object and its edge. Specifically, the model uses two cascaded partial decoders to form two initial estimates of the object segmentation map and its corresponding edge map. This is followed by a series of object decoders and edge decoders which work in conjunction with dual parallel reverse attention modules. The dual parallel reverse attention (DPRA) modules repeatedly prunes the features at multiple scales to put emphasis on the object segmentation and the edge segmentation respectively. Furthermore, we propose a novel decoder block that uses spatial and channel attention to combine features from the preceding decoder block and reverse attention (RA) modules for object and edge segmentation. We compare our model against popular segmentation models such as U-Net, SegNet and PraNet and demonstrate through a five fold cross validation experiment that our model improves the segmentation accuracy significantly on the Kvasir-SEG dataset and Kvasir-Instrument dataset.

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Kvasir-Instrument: Diagnostic and Therapeutic Tool Segmentation Dataset in Gastrointestinal Endoscopy

Cited by 53 publications

References 11 publications

Employing GRU to combine feature maps in DeeplabV3 for a better segmentation model

Employing GRU to combine feature maps in DeeplabV3 for a better segmentation model

Polyp and Surgical Instrument Segmentation with Double Encoder-Decoder Networks

Dual Parallel Reverse Attention Edge Network : DPRA-EdgeNet

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