CN112184719A - Fatty liver intelligent grading evaluation method based on abdominal CT - Google Patents
Fatty liver intelligent grading evaluation method based on abdominal CT Download PDFInfo
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- 208000004930 Fatty Liver Diseases 0.000 title claims abstract description 37
- 206010019708 Hepatic steatosis Diseases 0.000 title claims abstract description 37
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- 231100000240 steatosis hepatitis Toxicity 0.000 title claims abstract description 37
- 230000003187 abdominal effect Effects 0.000 title claims abstract description 21
- 238000011156 evaluation Methods 0.000 title abstract description 9
- 210000000952 spleen Anatomy 0.000 claims abstract description 56
- 230000011218 segmentation Effects 0.000 claims abstract description 35
- 210000004185 liver Anatomy 0.000 claims abstract description 30
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- 238000007781 pre-processing Methods 0.000 claims abstract description 5
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- 238000003745 diagnosis Methods 0.000 abstract description 5
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- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 238000013399 early diagnosis Methods 0.000 description 1
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- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/02—Devices for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/03—Computerised tomographs
- A61B6/032—Transmission computed tomography [CT]
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
- A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
- A61B6/5217—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data extracting a diagnostic or physiological parameter from medical diagnostic data
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Abstract
The invention relates to an abdominal CT-based fatty liver intelligent detection and grading evaluation method, which relates to the field of intelligent medical image diagnosis and comprises the following steps: reading a CT image of the abdomen of a patient, selecting 4 adjacent slices corresponding to the maximum areas of the liver and the spleen to construct a training sample set, and preprocessing data of the training sample set; building a U-Net segmentation network model, sending a training sample into the segmentation network model for supervised learning, and after the segmentation model is trained to be converged, segmenting the CT slice by using the model to segment liver tissues and spleen tissues in the slice; respectively carrying out gridding cutting on the liver tissue and the spleen tissue to obtain a plurality of rectangular small regions with the same area, randomly selecting 5 rectangular small regions in the two tissues as sampling regions, calculating respective gray average values as a liver CT value and a spleen CT value, and finally grading the fatty liver according to the liver/spleen CT ratio. The invention realizes the full-automatic segmentation of liver tissues and spleen tissues based on abdominal CT images, thereby carrying out intelligent grading evaluation on fatty liver.
Description
Technical Field
The invention relates to the field of intelligent medical image diagnosis, in particular to an intelligent grading evaluation method for fatty liver based on abdominal CT.
Background
The dietary structure and the activity mode of human beings are changed greatly at present, and the well-being life makes people obtain excessive fat in diet, but the physical labor is greatly reduced, which indirectly induces various diseases. Fatty liver, a common digestive disease, is due to abnormal metabolism of fat in liver cells caused by various reasons, resulting in excessive accumulation of fat. The fatty liver can be recovered to normal in early diagnosis and timely treatment, the fatty liver is qualitatively and quantitatively diagnosed clinically by adopting imaging technologies such as B-ultrasound, CT and the like, the fatty liver is mainly shown as liver density reduction due to fat accumulation during CT examination, the fatty liver is graded by adopting a liver/spleen CT ratio in the imaging diagnosis, the severe fatty liver is obtained when the liver/spleen CT ratio is less than 0.5, the moderate fatty liver is obtained when the liver/spleen CT ratio is less than 0.7, and the mild fatty liver is obtained when the liver/spleen CT ratio is less than 1, so that the severity of the fatty liver is evaluated, and the quantitative diagnosis is further given.
Currently, a research method for fat liver grading diagnosis is generally to manually segment liver tissues and spleen tissues by an imaging doctor, select a certain sub-region of the liver tissues and the spleen tissues, calculate a CT gray average value of the sub-region, and evaluate and grade the fat liver by using a CT ratio of the liver tissues and the spleen tissues. The manual segmentation of the CT image is time-consuming and labor-consuming, and a semi-automatic segmentation method is developed later, but the initial segmentation curve of the organ needs to be given on the CT image to realize the segmentation of the organ. In recent years, with the development of artificial intelligence and big data technology, the research of medical images based on deep learning has attracted much attention, and it is a trend to realize fully automatic segmentation of medical images using the deep learning technology.
Disclosure of Invention
The invention provides an intelligent grading evaluation method for fatty liver based on abdominal CT (computed tomography), which aims at the defects of the background art, and realizes full-automatic segmentation of liver tissues and spleen tissues by using a deep learning technology so as to grade the severity evaluation of the fatty liver.
The technical scheme adopted by the invention for solving the technical problems is as follows:
an intelligent fatty liver grading assessment method based on abdominal CT specifically comprises the following steps:
step 1, reading a CT image of the abdomen of a patient, selecting 4 adjacent slices corresponding to the maximum areas of the liver and the spleen to construct a training sample set, and preprocessing data of the training sample set;
and 3, respectively carrying out gridding cutting on the liver tissue and the spleen tissue to obtain a plurality of rectangular small regions with the same area, randomly selecting 5 rectangular small regions in the two tissues as sampling regions, calculating respective gray average values as a liver CT value and a spleen CT value, and finally grading the fatty liver according to the liver and spleen CT ratio.
As a further preferable scheme of the intelligent fatty liver grading assessment method based on abdominal CT of the present invention, the step 1 specifically comprises:
step 1.1: reading a DCM file of the abdominal CT image of a patient, and selecting 4 layers of slices near the maximal area of the liver and the spleen;
step 1.2: correcting the CT value of the slice by using a window width and window level technology, adjusting the size of the CT slice by using a scaling technology, and eliminating equipment background interference information in the image by using an image connected region method;
step 1.3: the imaging doctor manually segments the selected 4 layers of slices to obtain a mask map of liver tissues and spleen tissues, so as to construct a training sample data set.
As a further preferable scheme of the intelligent fatty liver grading assessment method based on abdominal CT of the present invention, the step 2 specifically comprises:
step 2.1: constructing a U-Net segmentation network model, wherein the U-Net network structure mainly comprises a convolution layer, a pooling layer, a cascading layer and an anti-convolution layer, the convolution layer utilizes convolution kernels to extract feature maps on an input image, the pooling layer is used for performing down-sampling operation on each feature map, the anti-convolution layer is used for performing convolution operation after filling the feature maps, and the cascading layer is used for performing combination operation on the two feature maps;
step 2.2: sending the training sample set into the segmentation network model for supervised learning, initializing weight parameters, setting learning rate, and iteratively training for multiple times by using an Adam optimizer until the segmentation model converges;
step 2.3: segmenting the CT slice by using the model to segment liver tissues and spleen tissues in the image;
as a further preferable scheme of the intelligent fatty liver grading assessment method based on abdominal CT of the present invention, the step 3 specifically comprises:
step 3.1: sequentially carrying out gridding cutting on the two parts of organ tissues after the division to obtain a plurality of rectangular small regions with the same area in the liver tissue and the spleen tissue;
step 3.2: randomly selecting 5 rectangular small areas in the two organ tissues as sampling areas of the organ tissues;
step 3.3: and respectively calculating the respective gray average values of the two organs as a liver CT value and a spleen CT value, and finally grading the fatty liver by using the liver-spleen CT ratio.
Through the technical scheme, compared with the prior art, the invention has the following beneficial effects:
(1) full automatic segmentation of liver and spleen tissues: and the full-automatic segmentation of each organ in the abdominal CT image is realized by utilizing a deep learning convolutional neural network model.
(2) Grading assessment of fatty liver: the mean value of the gray levels of a plurality of sub-areas in the organ tissue CT image is selected to calculate the liver/spleen CT ratio, and the evaluation result is more objectively given.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a flowchart of a method for abdominal CT-based intelligent fatty liver grading assessment according to an embodiment of the present invention;
FIG. 2 is a CT image of the abdomen of a patient in an embodiment of the present invention;
FIG. 3 is a flow chart of segmenting an organ using a U-Net model in an embodiment of the present invention;
FIG. 4 is a cut-away view of liver tissue and spleen tissue organs of a patient according to an embodiment of the present invention.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic views illustrating only the basic structure of the present invention in a schematic manner, and thus show only the constitution related to the present invention. Other advantages and features of the present invention will become readily apparent to those skilled in the art from the following detailed description, wherein it is to be understood that the invention is not limited to the specific embodiments disclosed, but is intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
FIG. 1 shows an intelligent grading assessment method for fatty liver based on abdominal CT. As shown, the method comprises the steps of:
step 1, preprocessing the CT image data of the abdomen of a patient, reading a DCM image file by using a Python programming development technology, converting the file into a three-dimensional array matrix, and carrying out data specification on the matrix, wherein the method specifically comprises the following steps:
step 1.1: selecting nearby 4 layers of slices corresponding to the maximum areas of the liver and the spleen, and reading a DCM file of the abdominal CT image of the patient by using a SimpleITK kit in a Python programming technology;
step 1.2: correcting the CT value of the slice by using a window width and window level technology, wherein the window width value is 400hu, the window level value is 100hu, adjusting the size of the CT slice by using a bilinear interpolation technology, the size of the slice after adjustment is 256 × 256, and eliminating equipment background interference information in the image by using an image connected region method;
step 1.3: the imaging doctor manually segments the selected 4 layers of slices to obtain a mask map of liver tissues and spleen tissues, so as to construct a training sample data set.
step 2.1: the method comprises the steps that a Keras framework toolkit is used for constructing a U-Net segmentation network model, the U-Net network structure mainly comprises a convolution layer, a pooling layer, a cascade layer and an anti-convolution layer, the convolution layer utilizes convolution kernels to extract feature maps on input images, the pooling layer is used for conducting down-sampling operation on each feature map, the anti-convolution layer is used for conducting convolution operation after filling up the feature maps, and the cascade layer is used for conducting combination operation on the two feature maps;
step 2.2: sending the training sample set into the segmentation network model for supervised learning, setting the batch size to be 1, setting the learning rate to be 0.001, using an Adam optimizer for iterative training, and setting the epoch times to be 10 until the segmentation model converges;
step 2.3: inputting a CT slice image, segmenting the CT slice by using the model, outputting a mask map of liver tissues and spleen tissues, and finally segmenting the liver tissues and the spleen tissues in the image;
step 3, respectively carrying out gridding cutting on the liver tissue and the spleen tissue to obtain a plurality of rectangular small regions with the same area, randomly selecting 5 rectangular small regions in the two tissues as sampling regions, calculating respective gray average values as a liver CT value and a spleen CT value, and finally grading the fatty liver according to the liver and spleen CT ratio, wherein the specific steps comprise:
step 3.1: sequentially carrying out gridding cutting on the two parts of organ tissues after the division to obtain a plurality of rectangular small regions with the same area in the liver tissue and the spleen tissue;
step 3.2: randomly selecting 5 rectangular small areas in the two organ tissues as sampling areas of the organ tissues;
step 3.3: and respectively calculating the respective gray average values of the two organs as a liver CT value and a spleen CT value, further calculating a liver and spleen CT ratio, and finally grading the fatty liver according to the ratio.
Example one
The invention discloses an intelligent fatty liver grading assessment method and system based on abdominal CT, which specifically comprises the following steps:
step 1, preprocessing the CT image data of the abdomen of a patient shown in fig. 2, reading a DCM image file by using a SimpleITK toolkit, converting the file into an array matrix by using a Numpy toolkit, correcting the CT value of a slice by using a window width window level technology, adjusting the size of the CT slice by using a bilinear interpolation value technology, adjusting the size of the slice to 256 x 256, and eliminating equipment background interference information in the image by using an image connected region method;
and 3, sequentially carrying out gridding cutting on the two parts of organ tissues after the division to obtain a plurality of rectangular small regions with the same area in the liver tissues and the spleen tissues, selecting 5 rectangular small regions in the two organ tissues as sampling regions of the organ tissues, respectively calculating the respective gray average values of the two organs as a liver CT value and a spleen CT value, further calculating the liver and spleen CT ratio, and giving a grading result of the fatty liver. Fig. 4 is a cut pattern of liver tissue and spleen tissue organs.
Based on the deep learning technology, the invention realizes the full-automatic segmentation of the liver tissue and the spleen tissue, is more intelligent than the traditional manual segmentation and semi-automatic segmentation, and further realizes the grading evaluation of the fatty liver based on the liver/spleen CT ratio.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.
Claims (4)
1. An intelligent fatty liver grading assessment method based on abdominal CT is characterized by comprising the following steps:
step 1, reading a CT image of the abdomen of a patient, selecting 4 adjacent slices corresponding to the maximum areas of the liver and the spleen to construct a training sample set, and preprocessing data of the training sample set;
step 2, constructing a U-Net segmentation network model, sending a training sample into the segmentation network model for supervised learning, and after the segmentation model is trained and converged, segmenting the CT image by using the model to segment liver tissues and spleen tissues in the image;
and 3, respectively carrying out gridding cutting on the liver tissue and the spleen tissue to obtain a plurality of rectangular small regions with the same area, randomly selecting 5 rectangular small regions in the two tissues as sampling regions, calculating respective gray average values as a liver CT value and a spleen CT value, and finally grading the fatty liver according to the liver and spleen CT ratio.
2. The abdominal CT-based intelligent grading assessment method for fatty liver, according to claim 1, wherein the step 1 specifically comprises:
step 1.1: reading a DCM file of the abdominal CT image of a patient, and selecting 4 layers of slices near the maximal area of the liver and the spleen;
step 1.2: correcting the CT value of the slice by using a window width and window level technology, adjusting the size of the CT slice by using a scaling technology, and eliminating equipment background interference information in the image by using an image connected region method;
step 1.3: the imaging doctor manually segments the selected 4 layers of slices to obtain a mask map of liver tissues and spleen tissues, so as to construct a training sample data set.
3. The abdominal CT-based fatty liver intelligent grading assessment method and system according to claim 1, wherein the step 2 specifically comprises:
step 2.1: constructing a U-Net segmentation network model, wherein the U-Net network structure mainly comprises a convolution layer, a pooling layer, a cascading layer and an anti-convolution layer, the convolution layer utilizes convolution kernels to extract feature maps on an input image, the pooling layer is used for performing down-sampling operation on each feature map, the anti-convolution layer is used for performing convolution operation after filling the feature maps, and the cascading layer is used for performing combination operation on the two feature maps;
step 2.2: sending the training sample set into the segmentation network model for supervised learning, initializing weight parameters, setting learning rate, and iteratively training for multiple times by using an Adam optimizer until the segmentation model converges;
step 2.3: the model is used to segment the CT slices, and the liver tissue and spleen tissue in the image are segmented.
4. The abdominal CT-based intelligent grading assessment method for fatty liver according to claim 1, wherein the step 3 specifically comprises:
step 3.1: sequentially carrying out gridding cutting on the two parts of organ tissues after the division to obtain a plurality of rectangular small regions with the same area in the liver tissue and the spleen tissue;
step 3.2: randomly selecting 5 rectangular small areas in the two organ tissues as sampling areas of the organ tissues;
step 3.3: and respectively calculating the respective gray average values of the two organs as a liver CT value and a spleen CT value, and finally grading the fatty liver by using the liver-spleen CT ratio.
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