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徐勝舟,程時宇.基于全卷積神經網絡遷移學習的乳腺腫塊圖像分割[J].中南民族大學學報自然科學版,2019,(2):278-284
基于全卷積神經網絡遷移學習的乳腺腫塊圖像分割
Breast mass image segmentation based on transfer learning of fully convolutional neural networks
  
DOI:10.12130/znmdzk.20190223
中文關鍵詞: 乳腺腫塊  分割  全卷積神經網絡  遷移學習
英文關鍵詞: breast mass  segmentation  fully convolutional neural network  transfer learning
基金項目:國家自然科學基金資助項目(61302192);中央高?;究蒲袠I務費專項資金項目(CZY19011)
作者單位
徐勝舟,程時宇 中南民族大學 計算機科學學院, 武漢430074 
摘要點擊次數: 198
全文下載次數: 192
中文摘要:
      針對乳腺X線攝片中腫塊通常會被周圍致密組織所掩蓋,對比度低,且其形狀不規則,腫塊圖像分割困難的問題,設計了一種基于全卷積神經網絡遷移學習的乳腺腫塊圖像分割方法.該方法首先對乳腺腫塊圖像進行數據增強,然后利用遷移學習,對設計的全卷積網絡模型載入參數并訓練分割模型,最后在訓練好的模型上對待分割圖像進行處理.分割結果采用區域面積重疊率、Dice相似系數、Hausdorff距離等指標進行評價分析,在公開數據集的483幅圖像上的實驗結果表明:提出的方法的分割效果明顯優于傳統分割算法.
英文摘要:
      Breast mass segmentation exists difficulties since masses in mammograms may appear with irregular shapes, low contrast and share a similar intensity distribution with the surrounding breast structures. A method of breast mass image segmentation based on transfer learning of full convolutional neural network is proposed. The method firstly makes data augmentation for the breast mass image, then uses the transfer learning to load the parameters of the designed full convolution neural network model and trains the segmentation model. Finally, the image to be segmented is processed on the trained model. The segmentation results are evaluated by the area overlap ratio, Dice similarity coefficient, Hausdorff distance and other indicators. The experimental results on the 483 images of the public data set indicate that the segmentation results of the proposed method are significantly better than the traditional segmentation algorithms.
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