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Discriminative features pyramid network for medical image segmentation

Xie Xiwang, Xie Lijie, Li Guanyu, Guo Hao, Zhang Weidong, Shao Feng, Zhao Wenyi, Tong Ling, Pan Xipeng, An Jubai

Biocybernetics and Biomedical Engineering

2024

Vol. 44, no. 2

327--340

The diverse shapes and scales, complicated backgrounds, blurred boundaries, and similar appearances challenge the current organ segmentation methods in medical scene images. It is difficult to acquire satisfactory performance to directly extend the object segmentation methods in the natural scene images to the medical scene images. In this paper, we propose a discriminant feature pyramid (DFPNet) network for organ segmentation in the original medical images, which consists of two sub-networks: the feature steered network and the border network. To be specific, the feature steered network takes a top-down step-wise manner to extract abundant context information, which is conducive to suppressing the cluttered background and perceiving the scale variation of objects. The border network utilizes a bottom-up step-wise manner to optimize the boundary feature map, which aims at distinguishing adjacent edge features with similar appearances but diverse labels. A series of experiments were conducted on three publicly available medical datasets ( i.e., LUNA 16, RIM-ONE-R1, and VNC datasets) to evaluate the validity and generalization of the proposed DFPNet. Experimental results indicate that our network achieves superior performance in terms of the receiver operating characteristic (ROC) curve, F-Score, Jaccard index, and Hausdorff distance. The code will be available at: https://github.com/Xie-Xiwang/DFPNet.

Experimental study of fuel combustion and emission characteristics of marine diesel engines using advanced fuels

Changxiong Li, Hu Yihuai, Yang Zy, Guo Hao

Polish Maritime Research

2023

nr 3

48--58

In order to explore the potential application of oxygenated fuels, polyoxymethylene dimethyl ethers (PODE), as an alternative fuel for marine diesel engines, the fuel combustion performance and gas emission characteristics of pure diesel oil, diesel-blended PODE, and pure PODE were tested on a marine diesel engine under different running conditions. The experimental results indicate that oxygen consumption can be reduced by diesel-blended PODE and pure PODE. The in-cylinder pressure and exothermic curve were consistent with the trend of diesel oil. Also, the ignition delay of diesel-blended PODE and pure PODE decreased, and the diffusion rate was accelerated, which helped to improve the combustion performance of diesel engines. Diesel blended PODE and pure PODE reduced the particulate matter (PM) emissions by up to 56.9% and 86.8%, respectively, and CO emissions by up to 51.1% and 56.3%, respectively. NOx emissions were gradually decreased with engine load. CO2 emissions were slightly increased, and the effective fuel consumption was increased up to 48% and 132%, respectively. It was shown that PODE could provide comparable power in a marine diesel engine and improve the fuel combustion and gas emission of the engine as a clean alternative fuel for marine diesel engines.

A machine learning method for soil conditioning automated decision-making of EPBM : hybrid GBDT and Random Forest Algorithm

Lin Lin, Guo Hao, Lv Yancheng, Liu Jie, Tong Changsheng, Yang Shuqin

Eksploatacja i Niezawodność

2022

Vol. 24, no. 2

237--247

There lacks an automated decision-making method for soil conditioning of EPBM with high accuracy and efficiency that is applicable to changeable geological conditions and takes drive parameters into consideration. A hybrid method of Gradient Boosting Decision Tree (GBDT) and random forest algorithm to make decisions on soil conditioning using foam is proposed in this paper to realize automated decision-making. Relevant parameters include decision parameters (geological parameters and drive parameters) and target parameters (dosage of foam). GBDT, an efficient algorithm based on decision tree, is used to determine the weights of geological parameters, forming 3 parameters sets. Then 3 decision-making models are established using random forest, an algorithm with high accuracy based on decision tree. The optimal model is obtained by Bayesian optimization. It proves that the model has obvious advantages in accuracy compared with other methods. The model can realize real-time decision-making with high accuracy under changeable geological conditions and reduce the experiment cost.