Tidal Flow: A Fast and Teachable Maximum Flow Algorithm

OLYMPIADS IN INFORMATICS ◽

10.15388/ioi.2018.03 ◽

2018 ◽

Vol 12 ◽

pp. 25-41

Author(s):

Matthew C. FONTAINE

Keyword(s):

Computer Science ◽

Tidal Flow ◽

Maximum Flow ◽

Efficient Algorithms ◽

Science Students ◽

Flow Algorithm ◽

Maximum Flows

Among the most interesting problems in competitive programming involve maximum flows. However, efficient algorithms for solving these problems are often difficult for students to understand at an intuitive level. One reason for this difficulty may be a lack of suitable metaphors relating these algorithms to concepts that the students already understand. This paper introduces a novel maximum flow algorithm, Tidal Flow, that is designed to be intuitive to undergraduate andpre-university computer science students.

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Integrating Improved U-Net and Continuous Maximum Flow Algorithm for 3D Brain Tumor Image Segmentation

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2020.64.4.040412 ◽

2020 ◽

Vol 64 (4) ◽

pp. 40412-1-40412-11

Author(s):

Kexin Bai ◽

Qiang Li ◽

Ching-Hsin Wang

Keyword(s):

Brain Tumor ◽

Data Augmentation ◽

A Priori ◽

Class Imbalance ◽

Maximum Flow ◽

Magnetic Resonance Images ◽

Tumor Segmentation ◽

Similarity Coefficients ◽

Segmentation Algorithms ◽

Flow Algorithm

Abstract To address the issues of the relatively small size of brain tumor image datasets, severe class imbalance, and low precision in existing segmentation algorithms for brain tumor images, this study proposes a two-stage segmentation algorithm integrating convolutional neural networks (CNNs) and conventional methods. Four modalities of the original magnetic resonance images were first preprocessed separately. Next, preliminary segmentation was performed using an improved U-Net CNN containing deep monitoring, residual structures, dense connection structures, and dense skip connections. The authors adopted a multiclass Dice loss function to deal with class imbalance and successfully prevented overfitting using data augmentation. The preliminary segmentation results subsequently served as the a priori knowledge for a continuous maximum flow algorithm for fine segmentation of target edges. Experiments revealed that the mean Dice similarity coefficients of the proposed algorithm in whole tumor, tumor core, and enhancing tumor segmentation were 0.9072, 0.8578, and 0.7837, respectively. The proposed algorithm presents higher accuracy and better stability in comparison with some of the more advanced segmentation algorithms for brain tumor images.

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