基于組織微陣列的乳腺癌自動診斷
發(fā)布時間：2018-12-19 10:01
【摘要】：在臨床醫(yī)院,基于乳腺癌組織病理圖像評分的諾丁漢分級系統(tǒng)是由病理醫(yī)生觀察分析腺管的形成、核的異型性以及有絲分裂次數(shù)這三個指標(biāo)得到的。由于經(jīng)驗和知識水平的差異,不同的醫(yī)生對病理組織切片評分可能會有差異。因此病理組織分析的計算機輔助診斷研究具有重要意義,它能夠為不同經(jīng)驗的臨床醫(yī)生提供客觀的診斷結(jié)果,也可以避免一些人為的漏檢。上皮和間質(zhì)組織是乳腺組織中最為基本的兩種組織,研究表明大約80%乳腺癌起源于上皮組織,因此上皮組織和間質(zhì)組織及其微環(huán)境的分析是評估乳腺癌風(fēng)險的重要標(biāo)志。因此上皮組織和間質(zhì)組織精確分割是構(gòu)建計算機輔助診斷系統(tǒng)的前提條件。針對以上問題,本文研究了基于組織微陣列的乳腺癌自動診斷方法,該方法主要分為乳腺組織微陣列的上皮與間質(zhì)組織的自動分割以及基于乳腺組織微陣列的癌癥等級自動評分兩個方面。具體內(nèi)容為:首先采用基于全卷積網(wǎng)絡(luò)的上皮與間質(zhì)組織自動分割方法,實現(xiàn)端到端的小尺寸圖像分割,從實驗分割評估結(jié)果看出:在荷蘭癌癥研究所(NKI)和溫哥華綜合醫(yī)院(VGH)提供的數(shù)據(jù)集上分割精確度(像素準(zhǔn)確率92.0%、像素平均準(zhǔn)確率85.7%、平均重疊率79.5%、類權(quán)重重疊率86.1%)最高,分割效果最好,同等條件下分割速度較快(0.097秒);然后通過滑動窗取塊的方式批量輸入網(wǎng)絡(luò)實現(xiàn)大尺寸的組織微陣列圖上皮與間質(zhì)自動分割。最后提取出組織微陣列圖像的上皮組織和間質(zhì)組織后,分別進(jìn)行提取特征(顏色直方圖、紋理)并且組合特征,再把組合特征輸入到支持向量機分類器中,得到病理分級結(jié)果(一級分類準(zhǔn)確率81.7%、二級的分類準(zhǔn)確率80.6%),針對實際的乳腺癌組織微陣列數(shù)據(jù)集實現(xiàn)乳腺癌組織病理自動分級,從而達(dá)到計算機輔助乳腺癌診斷的目標(biāo)。
[Abstract]:In clinical hospitals, the Nottingham grading system based on the pathological image of breast cancer was obtained by the pathologist's observation and analysis of the formation of the glandular duct, the heterogeneity of the nucleus and the frequency of mitosis. Due to differences in experience and level of knowledge, different doctors may have different scores on histopathological sections. Therefore, the computer-aided diagnosis of pathological tissue analysis is of great significance. It can provide objective diagnostic results for clinicians with different experiences, and avoid some artificial misdiagnosis. Epithelium and mesenchymal tissue are the two most basic tissues in mammary gland. Studies show that about 80% of breast cancer originated from epithelial tissue, so the analysis of epithelium and interstitial tissue and its microenvironment is an important marker to assess the risk of breast cancer. Therefore, accurate segmentation of mesenchymal tissue and epithelial tissue is the precondition of computer aided diagnosis system. In view of the above problems, this paper studies the automatic diagnosis method of breast cancer based on tissue microarray. This method is mainly divided into two aspects: the automatic segmentation of epithelial and interstitial tissue of breast tissue microarray and the automatic grading of cancer grade based on breast tissue microarray. The main contents are as follows: firstly, an automatic segmentation method of epithelium and mesenchymal tissue based on full convolution network is adopted to realize end to end small size image segmentation. From the results of the experimental segmentation evaluation, we can see the accuracy of segmentation on the data set provided by the Dutch Cancer Institute (NKI) and the Vancouver General Hospital (VGH) (the pixel accuracy is 92.0, the average pixel accuracy is 85.775, The average overlap rate was 79.5%, the class weight overlap rate was 86.1%), the segmentation effect was the best, and the speed of segmentation was faster (0.097 seconds) under the same conditions; Then the large-scale tissue microarray epithelium and mesenchymal tissue are automatically segmented by bulk input network with sliding window. Finally, after extracting the epithelium and mesenchymal tissue of the tissue microarray image, the features (color histogram, texture) are extracted, and then the combined features are input into the support vector machine classifier. The results of pathological grading (81.7% of primary classification accuracy and 80.6% of second-level classification accuracy) were obtained. According to the actual breast cancer tissue microarray data set, the automatic classification of breast cancer tissue was realized. In order to achieve the goal of computer-aided breast cancer diagnosis.
【學(xué)位授予單位】：南京信息工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R737.9;TP391.41
本文編號：2386767
[Abstract]:In clinical hospitals, the Nottingham grading system based on the pathological image of breast cancer was obtained by the pathologist's observation and analysis of the formation of the glandular duct, the heterogeneity of the nucleus and the frequency of mitosis. Due to differences in experience and level of knowledge, different doctors may have different scores on histopathological sections. Therefore, the computer-aided diagnosis of pathological tissue analysis is of great significance. It can provide objective diagnostic results for clinicians with different experiences, and avoid some artificial misdiagnosis. Epithelium and mesenchymal tissue are the two most basic tissues in mammary gland. Studies show that about 80% of breast cancer originated from epithelial tissue, so the analysis of epithelium and interstitial tissue and its microenvironment is an important marker to assess the risk of breast cancer. Therefore, accurate segmentation of mesenchymal tissue and epithelial tissue is the precondition of computer aided diagnosis system. In view of the above problems, this paper studies the automatic diagnosis method of breast cancer based on tissue microarray. This method is mainly divided into two aspects: the automatic segmentation of epithelial and interstitial tissue of breast tissue microarray and the automatic grading of cancer grade based on breast tissue microarray. The main contents are as follows: firstly, an automatic segmentation method of epithelium and mesenchymal tissue based on full convolution network is adopted to realize end to end small size image segmentation. From the results of the experimental segmentation evaluation, we can see the accuracy of segmentation on the data set provided by the Dutch Cancer Institute (NKI) and the Vancouver General Hospital (VGH) (the pixel accuracy is 92.0, the average pixel accuracy is 85.775, The average overlap rate was 79.5%, the class weight overlap rate was 86.1%), the segmentation effect was the best, and the speed of segmentation was faster (0.097 seconds) under the same conditions; Then the large-scale tissue microarray epithelium and mesenchymal tissue are automatically segmented by bulk input network with sliding window. Finally, after extracting the epithelium and mesenchymal tissue of the tissue microarray image, the features (color histogram, texture) are extracted, and then the combined features are input into the support vector machine classifier. The results of pathological grading (81.7% of primary classification accuracy and 80.6% of second-level classification accuracy) were obtained. According to the actual breast cancer tissue microarray data set, the automatic classification of breast cancer tissue was realized. In order to achieve the goal of computer-aided breast cancer diagnosis.
【學(xué)位授予單位】：南京信息工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R737.9;TP391.41
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