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Faster RCNN和LGDF結合的肝包蟲病CT圖像病灶分割
2021年電子技術應用第7期
劉志華1,王正業1,李豐軍2,嚴傳波2
1.新疆醫科大學 公共衛生學院,新疆 烏魯木齊830011;2.新疆醫科大學 醫學工程技術學院,新疆 烏魯木齊830011
摘要: 針對人工閱片工作量大、閱片質量不佳且容易出現漏檢、錯判等問題,將Faster RCNN目標檢測模型應用于肝包蟲病CT圖像的檢測,并對目標檢測模型進行改進:基于圖片分辨率低、病灶大小不同的特點,使用網絡深度更深的殘差網絡(ResNet101)代替原來的VGG16網絡,用以提取更豐富的圖像特征;根據目標檢測模型得出的病灶坐標信息引入LGDF模型進一步對病灶進行分割,從而輔助醫生更高效的診斷疾病。實驗結果表明,基于ResNet101特征提取網絡的目標檢測模型能夠有效提取目標的特征,檢測準確率相比原始檢測模型提高2.1%,具有較好的檢測精度。同時,將病灶坐標信息引入LGDF模型,相比于原始的LGDF模型更好地完成了對肝包蟲病病灶的分割,Dice系數提高了5%,尤其對多囊型肝包蟲病CT圖像的分割效果較好。
中圖分類號: TN911.73;TP751.1
文獻標識碼: A
DOI:10.16157/j.issn.0258-7998.200923
中文引用格式: 劉志華,王正業,李豐軍,等. Faster RCNN和LGDF結合的肝包蟲病CT圖像病灶分割[J].電子技術應用,2021,47(7):33-37,43.
英文引用格式: Liu Zhihua,Wang Zhengye,Li Fengjun,et al. CT image segmentation of liver hydatid disease based on Faster RCNN and LGDF[J]. Application of Electronic Technique,2021,47(7):33-37,43.
CT image segmentation of liver hydatid disease based on Faster RCNN and LGDF
Liu Zhihua1,Wang Zhengye1,Li Fengjun2,Yan Chuanbo2
1.College of Public Health,Xinjiang Medical University,Urumqi 830011,China; 2.College of Medical Engineering Technology,Xinjiang Medical University,Urumqi 830011,China
Abstract: In view of the large workload of manual image reading, poor image reading quality, and prone to missed inspections and wrong judgments,in this paper, the faster RCNN target detection model is applied to the detection of hepatic echinococcosis CT images. And the target detection model is improved: based on the characteristics of low image resolution and different lesion sizes, the residual network with deeper network depth(ResNet101) is used to replace the original VGG16 to extract richer image features; according to the coordinate information of the lesion obtained by the object detection model, the LGDF model is introduced to further segment the lesion to assist doctors in diagnosing the disease more efficiently. The experimental results show that the object detection model based on the ResNet101 feature extraction network can effectively extract the features of the target, and the detection accuracy is 2.1% higher than the original detection model, and it has better detection accuracy. At the same time, the coordinate information of the lesion is introduced into the LGDF model. Compared with the original LGDF model, the segmentation of hepatic hydatid lesions is better completed, the Dice coefficient is increased by 5%, and the segmentation effect is better especially for the multi cystic liver hydatidosis CT image.
Key words : faster RCNN;LGDF;deep learning;object detection;lesion segmentation

0 引言

    肝包蟲病(Hepatic Echinococcosis,HE)又稱棘球幼病,是一種人畜共患寄生蟲病,主要流行于畜牧業發達地區[1-3]。肝包蟲病患者在患病初期無特異性的癥狀及體征,隨著包囊的生長,患者出現臨床癥狀,引起自身機體的感染并發生一些并發癥,其中部分并發癥可能危及患者生命,需要醫生的及時診斷和緊急干預[4-5]。醫學影像學檢查是診斷疾病的一種方式,能夠為患者的病情提供有用的信息,對于肝包蟲病的影像學診斷是由醫生查看拍攝的CT圖片診斷患者是否發生疾病。隨著影像設備的更新和發展,醫院每天產出大量的醫學圖片,醫生閱片時容易發生視覺疲勞現象,往往出現診斷效率低下、漏檢、誤判等問題。因此,本文基于目標檢測方法實現肝包蟲病病灶的檢測,從而輔助醫生智能診斷疾病。




本文詳細內容請下載:http://www.cdfla.com/resource/share/2000003650




作者信息:

劉志華1,王正業1,李豐軍2,嚴傳波2

(1.新疆醫科大學 公共衛生學院,新疆 烏魯木齊830011;2.新疆醫科大學 醫學工程技術學院,新疆 烏魯木齊830011)




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