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首頁> 美國衛(wèi)生研究院文獻>Journal of Digital Imaging >Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks
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Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks

機譯：基于超像素和卷積神經網絡的CT掃描自動器官分割

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Accurate segmentation of specific organ from computed tomography (CT) scans is a basic and crucial task for accurate diagnosis and treatment. To avoid time-consuming manual optimization and to help physicians distinguish diseases, an automatic organ segmentation framework is presented. The framework utilized convolution neural networks (CNN) to classify pixels. To reduce the redundant inputs, the simple linear iterative clustering (SLIC) of super-pixels and the support vector machine (SVM) classifier are introduced. To establish the perfect boundary of organs in one-pixel-level, the pixels need to be classified step-by-step. First, the SLIC is used to cut an image into grids and extract respective digital signatures. Next, the signature is classified by the SVM, and the rough edges are acquired. Finally, a precise boundary is obtained by the CNN, which is based on patches around each pixel-point. The framework is applied to abdominal CT scans of livers and high-resolution computed tomography (HRCT) scans of lungs. The experimental CT scans are derived from two public datasets (Sliver 07 and a Chinese local dataset). Experimental results show that the proposed method can precisely and efficiently detect the organs. This method consumes 38 s/slice for liver segmentation. The Dice coefficient of the liver segmentation results reaches to 97.43%. For lung segmentation, the Dice coefficient is 97.93%. This finding demonstrates that the proposed framework is a favorable method for lung segmentation of HRCT scans.
機譯：從計算機斷層掃描（CT）掃描準確分割特定器官是準確診斷和治療的基本且至關重要的任務。為了避免費時的手動優(yōu)化并幫助醫(yī)生區(qū)分疾病，提出了一種自動器官分割框架。該框架利用卷積神經網絡（CNN）對像素進行分類。為了減少冗余輸入，引入了超像素的簡單線性迭代聚類（SLIC）和支持向量機（SVM）分類器。為了在一個像素級別上建立器官的完美邊界，需要對像素進行逐步分類。首先，SLIC用于將圖像切成網格并提取相應的數(shù)字簽名。接下來，通過SVM對簽名進行分類，并且獲取粗糙邊緣。最終，CNN獲得了精確的邊界，該邊界基于每個像素點周圍的斑塊。該框架適用于肝臟的腹部CT掃描和肺部的高分辨率計算機斷層掃描（HRCT）掃描。實驗性CT掃描來自兩個公共數(shù)據集（Sliver 07和中國本地數(shù)據集）。實驗結果表明，該方法能夠準確，高效地檢測器官。此方法需要38 s /切片進行肝臟分割。肝臟分割結果的Dice系數(shù)達到97.43％。對于肺分割，Dice系數(shù)為97.93％。這一發(fā)現(xiàn)表明，所提出的框架是HRCT掃描的肺分割的理想方法。

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