本文選題：全掃描病理圖像 + 多種類型組織　； 參考：《中國(guó)生物醫(yī)學(xué)工程學(xué)報(bào)》2017年05期

【摘要】：結(jié)直腸全掃描圖像處理困難,原因在于圖像的數(shù)據(jù)量大、結(jié)構(gòu)復(fù)雜、信息含量多。目前對(duì)于結(jié)直腸癌組織病理圖像的研究通常包含腫瘤和基質(zhì)兩種組織類型,只有一小部分研究可以解決多種組織的問(wèn)題,但又不是處理全掃描的結(jié)直腸病理圖像。提出一種基于深度卷積網(wǎng)絡(luò)的結(jié)直腸全掃描病理圖像進(jìn)行多種類型組織分割的模型。該模型使用的網(wǎng)絡(luò)層數(shù)有8層,利用深度卷積網(wǎng)絡(luò)學(xué)習(xí)結(jié)直腸全掃描圖像中典型的8種類型的組織,利用訓(xùn)練好的模型對(duì)這8種類型的結(jié)直腸組織進(jìn)行分類測(cè)試,其測(cè)試集分類準(zhǔn)確率達(dá)92.48%。利用該模型對(duì)結(jié)直腸全掃描病理圖像中的8種類型組織進(jìn)行分割,首先對(duì)全掃描圖像進(jìn)行預(yù)處理,分成5000像素×5000像素大小的圖像塊,然后標(biāo)記出每一張中的8種類型的組織,最后將所得到的標(biāo)記結(jié)果進(jìn)行拼接,從而獲得整張結(jié)直腸全掃描病理圖像的8種類型組織的標(biāo)記結(jié)果。該方法對(duì)8種類型的組織分割的準(zhǔn)確率比較高,有一定輔助診斷的幫助。
[Abstract]:The total scanning of colorectal images is difficult due to the large amount of data, complex structure, and more information content. At present, the study of the histopathological images of colorectal cancer usually includes two types of tissues and tumors. Only a small part of the study can solve the problems of various tissues, but it is not a total scan of colon disease. A model of multiple types of tissue segmentation based on deep convolution network is proposed. The number of network layers used in this model is 8 layers, and 8 typical types of tissues in the total scanning image of colorectal cancer are studied by deep convolution network, and the 8 types of colorectal groups are used by the trained model. The classification test of the fabric is carried out. The classification accuracy of the test set is 92.48%.. The model is used to segment 8 types of tissues in the total scan pathological image of the colorectal. First, the full scan image is preprocessed into 5000 pixels * 5000 pixel size image blocks, and then the 8 types of tissues in each of the images will be marked. Finally, the results will be obtained. The labeling results were spliced to obtain the results of 8 types of tissues of the entire colorectal scan. The accuracy of the 8 types of tissue segmentation was higher and the help of a certain auxiliary diagnosis.

【作者單位】： 南京信息工程大學(xué)江蘇省大數(shù)據(jù)分析技術(shù)重點(diǎn)實(shí)驗(yàn)室;南方醫(yī)科大學(xué)病理學(xué)系;
【基金】：國(guó)家自然科學(xué)基金(61771249) 江蘇省“六大人才高峰”高層次人才項(xiàng)目(2013-XXRJ-019) 江蘇省自然科學(xué)基金(BK20141482)
【分類號(hào)】：R735.34;TP391.41

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1 王冠皓;深度卷積網(wǎng)絡(luò)及其在乳腺病理圖像分析中的應(yīng)用[D];南京信息工程大學(xué);2015年

2 龔磊;基于病理圖像的乳腺腫瘤定量化分析[D];南京信息工程大學(xué);2016年

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