Polyp-Net: A Multimodel Fusion Network for Polyp Segmentation
| Autor: | Debapriya Banik, Debotosh Bhattacharjee, Mita Nasipuri, Ondrej Krejcar, Kaushiki Roy |
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| Rok vydání: | 2021 |
| Předmět: |
Colorectal cancer
Computer science business.industry 020208 electrical & electronic engineering Histopathological analysis Feature extraction Cancer Pattern recognition 02 engineering and technology Image segmentation medicine.disease Malignancy Convolutional neural network Reduction (complexity) Wavelet Hausdorff distance 0202 electrical engineering electronic engineering information engineering medicine Segmentation Artificial intelligence Electrical and Electronic Engineering business Instrumentation |
| Zdroj: | IEEE Transactions on Instrumentation and Measurement. 70:1-12 |
| ISSN: | 1557-9662 0018-9456 |
| Popis: | Computer-aided diagnosis of disease primarily depends on proper vision-based measurement (VBM). The traditional approach followed for diagnosis of colorectal cancer includes a manual screening of colorectum via a colonoscope and resection of polyps for histopathological analysis to decide the grade of malignancy. This procedure is time-consuming and expensive, and removal of benign polyp for analysis signifies the inefficiency of the diagnosis system. These drawbacks inspired us to develop an automatic vision-based analysis method for preliminary in vivo malignancy analysis of the polyp region. In this work, we have proposed a fusion-based polyp segmentation network, namely, Polyp-Net. Recently, convolutional neural networks (CNNs) have shown immense success in the domain of medical image analysis as it can exploit in-depth significant features with high discrimination capabilities. Therefore, motivated by these insights, we have proposed an enriched version of CNN with a nascent pooling mechanism, namely dual-tree wavelet pooled CNN (DT-WpCNN). The resultant segmented mask contains some surplus high-intensity regions apart from the polyp region. These shortcomings are avoided using a new variation of the region-based level-set method, namely, the local gradient weighting-embedded level-set method (LGWe-LSM), which shows a significant reduction of false-positive rate. The pixel-level fusion of the two enhanced methods shows more potentiality in the segmentation of the polyp regions. Our proposed network is trained on CVC-colon DB and tested on CVC-clinic DB. It achieves a dice score of 0.839, volume-similarity of 0.863, precision of 0.836, recall of 0.811, F1-score of 0.823, F2-score of 0.815, and Hausdorff distance of 21.796 which outperforms the existing baseline CNN’s and recent state-of-the-art methods. |
| Databáze: | OpenAIRE |
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