CN111524122B - Method for constructing gauze blood-soaking amount estimation model based on characteristic engineering - Google Patents

Method for constructing gauze blood-soaking amount estimation model based on characteristic engineering Download PDF

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CN111524122B
CN111524122B CN202010324238.5A CN202010324238A CN111524122B CN 111524122 B CN111524122 B CN 111524122B CN 202010324238 A CN202010324238 A CN 202010324238A CN 111524122 B CN111524122 B CN 111524122B
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钟坤华
易斌
陈芋文
张力戈
李雨捷
支鸿羽
杨智勇
鲁开智
张矩
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Abstract

The invention relates to a method for constructing a gauze blood leaching amount estimation model based on characteristic engineering, and belongs to the field of artificial intelligence. The method comprises the following steps: s1, collecting blood soaking gauze images containing blood volume labels to construct a data set; s2, preprocessing the image, including image size regulation and color space conversion; s3, performing mask extraction on the image blood soaking area, and obtaining the image blood soaking area; s4, extracting blood-soaked gauze image features, including 14 features of hemoglobin amount in a blood-soaked area and mean value and variance of each channel in HSV color space, and further constructing an image feature set; s5 constructs a machine learning model of the gauze bleeding amount estimation based on the set of image features constructed in step S4. The invention is based on characteristic engineering, and can quickly and accurately estimate the intraoperative blood loss of the patient through the constructed model.

Description

Method for constructing gauze blood-soaking amount estimation model based on characteristic engineering
Technical Field
The invention belongs to the field of artificial intelligence, and relates to a method for constructing a gauze blood-soaking amount estimation model based on characteristic engineering.
Background
With the development of surgical techniques, there are about 3 hundred million surgical operations per year, with a post-operative complication rate of about 16.7%, and a mortality rate of about 0.5% of the work of anesthesiologists being critical in surgical operations, the most important and difficult task being to continuously monitor and assess blood loss during the operation, which can guide not only the first blood transfusion but also the second. If the intraoperative blood loss is underestimated, transfusion failure and postoperative complications may result. Blood loss during overestimation can lead to over-monitoring, transfusion-related complications and waste of blood products.
Intraoperative blood loss refers to a reduction in circulating blood volume, wherein the lost circulating blood volume includes the invisible components: blood (plasma) and visible components (mainly red blood cells). Intraoperative blood loss mainly includes bleeding or oozing from surgical platforms, blood content in gauze, aspirators and sterile towels. The biggest challenge in continuous intraoperative monitoring of blood loss is estimating the blood content in the gauze.
The main methods of estimating the blood absorbed by gauze include a visual evaluation method and a weighing method. Visual assessment method blood loss was estimated by visually measuring the area of blood on different sizes of surgical gauze. This method is the most commonly used method for clinical assessment of blood loss, but it is less accurate. When the blood loss is less than 150ml, it is easy to overestimate the blood loss. However, when the amount of blood lost is more than 300ml, the amount of blood lost is easily underestimated, and the more blood lost, the less apt to underestimate blood lost. The above method relates only to the amount of blood and does not take into account the different haemoglobin concentrations of the different patients and the dilution of the blood with saline during the operation, so that the capacity of this method to estimate the amount of visible components in the blood-soaked gauze is limited. The weighing method is relatively accurate, but the operation is complex, generally performed after the operation, and is inconvenient for quick intraoperative evaluation. Meanwhile, the weighing method needs the instrument nurse and the surgeon to accurately calculate the irrigation amount of the gauze, which has certain limitation in clinical application.
At present, artificial intelligence technology has been widely studied and applied in the medical field. In order to overcome the defects of the current method for evaluating the blood volume of blood soaked gauze to assist anaesthetists, a novel method based on characteristic engineering is provided. The method utilizes an image processing technology and hemoglobin data of a patient to extract key characteristics of the blood-soaked gauze image, and combines a characteristic engineering method to construct a model, so that the rapid and accurate estimation of the blood-soaked amount of the gauze can be realized.
Disclosure of Invention
In view of this, the present invention aims to provide a method for constructing a gauze blood-soaking amount estimation model based on feature engineering. The method utilizes the characteristic engineering to extract the key characteristics in the blood soaked gauze image, constructs a gauze blood soaking amount estimation model through a machine learning algorithm, and can realize the rapid and accurate estimation of the gauze blood volume in the operation process.
In order to achieve the purpose, the invention provides the following technical scheme:
a method for constructing a gauze blood-soaking amount estimation model based on feature engineering comprises the following steps:
s1: collecting a blood soaking gauze image containing blood volume labels, and constructing a data set;
s2: image preprocessing, including image size normalization and color space conversion;
s3: extracting a mask of the image blood soaking area, and obtaining the image blood soaking area;
s4: extracting blood-soaked gauze image characteristics, including 14 characteristics of hemoglobin amount in a blood-soaked area and mean value and variance of each channel in HSV color space, and constructing an image characteristic set;
s5: based on the set of image features constructed in step S4, a machine learning model of the gauze bleeding amount estimation is constructed.
Optionally, in step S1, when the blood-soaked gauze image is finished, the gauze is spread flatly, and the blood-soaked gauzes are photographed one by one.
Optionally, in the step S2, the size of the captured image is adjusted to 480 × 480 pixels, and the adjusted image is converted from the RGB color space to the HSV color space.
Optionally, in step S3, an image blood-soaking area mask is extracted according to the H value in the HSV color space, and two masks are specifically defined as:
Figure BDA0002462591430000021
Figure BDA0002462591430000022
where (i, j) is the pixel position of the blood-soaked image in the RGB color space.
Optionally, in the step S3, two blood-soaked areas of the blood-soaked gauze image are obtained by using a mask, which is defined as:
Figure BDA0002462591430000023
Figure BDA0002462591430000024
wherein (i, j) is the pixel position of the blood-soaked image in RGB color space, Bi,jAnd (3) obtaining HSV pixel vector values of the positions of the original blood-soaked gauze pictures (i, j).
Optionally, in step S4, the hemoglobin amount is obtained by multiplying a ratio of hemoglobin concentration to blood-immersed region area, and the specific steps include:
(1) calculating the number of pixels under the two blood soaking area masks, and respectively recording as PR1numAnd PR2num；
(2) The ratio of the blood-soaked area under the two masks is calculated respectively, namely the ratio of the whole image is calculated:
Figure BDA0002462591430000031
Figure BDA0002462591430000032
(3) normalization treatment of the patient hemoglobin concentration:
Figure BDA0002462591430000033
wherein Hbc represents the hemoglobin concentration of a current single patient, and Max and Min represent the maximum value and the minimum value of the hemoglobin concentrations of all patients respectively;
(4) the hemoglobin amount of the blood-soaked area under the two masks is respectively calculated and is defined as the product of the area ratio of the blood-soaked area and the normalized hemoglobin concentration:
Hgb1＝Hbc×AR1，
Hgb2＝Hbc×AR2。
optionally, in the step S4, for each blood-soaked gauze image, a mean and a variance of each channel of the blood-soaked area in the HSV color space are calculated, and since there are two blood-soaked area masks, mean and variance features are calculated for the two generated blood-soaked areas respectively; these features are noted as: h1_ mean, H1_ std, S1_ mean, S1_ std, V1_ mean, V1_ std, H2_ mean, H2_ std, S2_ mean, S2_ std, V2_ mean, V2_ std, 12 features in total.
Optionally, in the step 4, in each blood-soaked gauze image, 14 features are extracted in total, including the features Hgb1 and Hgb2, and the 12 features; extracting features from all pictures in the data set constructed in the step S1 to form an image feature set; and serially constructing the image feature set or parallelly constructing the image feature set.
Optionally, in the step 5, the gauze blood-soaking amount estimation machine learning model is a multiple linear regression model constructed on the constructed image feature set.
The invention has the beneficial effects that: according to the method for constructing the gauze blood immersion amount estimation model, the blood immersion gauze image is collected and marked, the gauze blood immersion amount is constructed, a training data set is constructed, the image size is regulated through image preprocessing, color space conversion is carried out, image characteristics are extracted based on characteristic engineering and combined with hemoglobin concentration information of a patient, a multiple linear regression gauze blood immersion amount estimation model is constructed by taking a quadratic error function as a loss function, the gauze blood immersion amount can be quickly and accurately estimated, accurate reference is provided for anesthesiologists, and the safety of the patient is improved.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a schematic flow chart of a method for constructing a gauze blood-soaking amount estimation model based on feature engineering;
FIG. 2 is a schematic diagram of an image of blood-soaked gauze according to an embodiment;
FIG. 3 is a schematic diagram showing an image of a blood-soaked area in the embodiment;
FIG. 4 is a flowchart of an embodiment.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
As shown in fig. 1, a method for constructing a gauze blood-soaking amount estimation model based on feature engineering includes the following steps:
s1, collecting blood soaking gauze images containing blood volume labels to construct a data set;
s2, preprocessing the image, including image size regulation and color space conversion;
s3, performing mask extraction on the image blood soaking area, and obtaining the image blood soaking area;
s4, extracting blood-soaked gauze image features, including 14 features of hemoglobin amount in a blood-soaked area and mean value and variance of each channel in HSV color space, and further constructing an image feature set;
s5 constructs a machine learning model of the gauze bleeding amount estimation based on the set of image features constructed in step S4.
Further, in step S1, when the operation is finished, the blood-soaked gauze image is obtained by spreading the gauze flatly and shooting the blood-soaked gauze one by one.
Further, in step S2, the size of the captured image is adjusted to 480 × 480 pixels, and the adjusted image is converted from the RGB color space to the HSV color space.
Further, in step S3, extracting an image blood-soaked area mask according to the H value in the HSV color space, and the two masks are specifically defined as:
Figure BDA0002462591430000051
Figure BDA0002462591430000052
where (i, j) is the pixel position of the blood-soaked image in the RGB color space.
Further, in the step S3, two blood-soaked areas of the blood-soaked gauze image are obtained by using the mask, which are defined as:
Figure BDA0002462591430000053
Figure BDA0002462591430000054
wherein (i, j) is the pixel position of the blood-soaked image in RGB color space, Bi,jAnd (3) obtaining HSV pixel vector values of the positions of the original blood-soaked gauze pictures (i, j). In practice, BR1 and BR2 are two blood-soaked images obtained from the original blood-soaked gauze image, each image having only red and black, red indicating the blood-soaked area and black indicating the non-blood-soaked area.
Further, in step S4, the hemoglobin amount is obtained by multiplying the ratio of the hemoglobin concentration to the blood-immersed region area, and the specific steps include:
(1) calculating the number of pixels under the two blood soaking area masks, and respectively recording as PR1numAnd PR2num；
(2) The ratio of the blood-soaked area under the two masks is calculated respectively, namely the ratio of the whole image is calculated:
Figure BDA0002462591430000055
Figure BDA0002462591430000056
(3) normalization treatment of the patient hemoglobin concentration:
Figure BDA0002462591430000057
wherein Hbc represents the hemoglobin concentration of a current single patient, and Max and Min represent the maximum value and the minimum value of the hemoglobin concentrations of all patients respectively;
(4) the hemoglobin amount of the blood-soaked area under the two masks is respectively calculated and is defined as the product of the area ratio of the blood-soaked area and the normalized hemoglobin concentration:
Hgb1＝Hbc×AR1，
Hgb2＝Hbc×AR2。
further, in the step S4, for each blood-soaked gauze image, the mean and variance of each channel in the HSV color space of the blood-soaked area are calculated, and since there are two blood-soaked area masks, the mean and variance features are calculated for the two generated blood-soaked areas respectively. These features are noted as: h1_ mean, H1_ std, S1_ mean, S1_ std, V1_ mean, V1_ std, H2_ mean, H2_ std, S2_ mean, S2_ std, V2_ mean, V2_ std, 12 features in total.
Further, in the step 4, a total of 14 features are extracted from each blood-soaked gauze image, including the features Hgb1 and Hgb2 in claim 6 and 12 features in claim 7. Features are extracted from all pictures in the data set constructed in step S1 to form an image feature set. The image feature set may be constructed serially or in parallel.
Further, in the step S5, the gauze blood immersion amount estimation machine learning model is a multiple linear regression model constructed on the image feature set constructed in the step S4. The multiple linear regression model is defined as:
yest＝β0+β1×f1+β2×f2+…+β14×f14，
wherein, yestFor blood volume estimation, beta0,β1,…,β14Is a parameter, f1,f2,…,f14Is the characteristic of the blood-soaked gauze image. The model solution uses a least square method to optimize the following loss functions:
Figure BDA0002462591430000061
wherein n is the number of images in the training set.
The preferred embodiment of the present invention will be described in detail with reference to fig. 1.
Example (b):
a method for constructing a gauze blood-soaking amount estimation model based on characteristic engineering is adopted to construct the gauze blood-soaking amount estimation model, and comprises the following steps:
step one, with reference to fig. 2, firstly resizing a 3968 × 2976 × 3 original image matrix to 480 × 480 × 3, and then converting the resized RGB image into an HSV space.
Step two, firstly dividing a blood soaking area and a non-blood soaking area according to H, S, V values in an HSV space, wherein the blood soaking area is divided into two parts:
Figure BDA0002462591430000071
Figure BDA0002462591430000072
two masks (the blood-immersed region is assigned as "255" and the non-blood-immersed region is assigned as "0") are obtained by the above two divisions, and the ratio of the blood-immersed region is calculated using the value of the H space.
And step three, obtaining two blood soaking area images by using the mask obtained in the HSV space, as shown in figure 3.
And step four, respectively calculating the mean value and the variance of the H, S, V channels of the two blood soaking area images in the HSV space, wherein the total number of the features is 12.
And step five, normalizing the hemoglobin concentration, and multiplying the normalized hemoglobin concentration by the ratio of the two blood soaking areas to obtain characteristics Hgb1 and Hgb 2.
And step six, executing the step one to the step five to all the images in the training set, wherein each image obtains 14 features to form an image feature set. And the processing can be carried out in series or in parallel.
And seventhly, training a multiple linear regression model by using the image feature set obtained in the sixth step aiming at the following quadratic loss function:
Figure BDA0002462591430000073
the trained model can be used for estimating the blood soaking amount of the gauze.
The flow chart of the embodiment is shown in fig. 4.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.

Claims (9)

1. A method for constructing a gauze blood-soaking amount estimation model based on characteristic engineering is characterized by comprising the following steps: the method comprises the following steps:
s1: collecting a blood soaking gauze image containing blood volume labels, and constructing a data set;
s2: image preprocessing, including image size normalization and color space conversion;
s3: extracting a mask of the image blood soaking area, and obtaining the image blood soaking area; the method comprises the following specific steps: extracting a blood soaking area mask of the image according to the H value in the HSV color space, wherein the two masks are total; obtaining two blood-soaking areas of the blood-soaking gauze image by using the mask;
s4: extracting blood-soaking gauze image characteristics, and aiming at each blood-soaking area, extracting the image characteristics: comprises 14 characteristics of hemoglobin amount in a blood soaking area, mean value and variance of each channel of HSV color space,
s5: based on the set of image features constructed in step S4, a machine learning model of the gauze bleeding amount estimation is constructed.
2. The method for constructing a gauze blood soaking amount estimation model based on feature engineering as claimed in claim 1, wherein: in step S1, when the operation is completed, the blood-soaked gauze image is flatly spread, and the blood-soaked gauzes are photographed one by one.
3. The method for constructing a gauze blood soaking amount estimation model based on feature engineering as claimed in claim 1, wherein: in the step S2, the captured image is resized to 480 × 480 pixel size, and the resized image is converted from the RGB color space to the HSV color space.
4. The method for constructing a gauze blood soaking amount estimation model based on feature engineering as claimed in claim 1, wherein: in step S3, an image blood-soaked area mask is extracted according to the H value in the HSV color space, and two masks are specifically defined as:
first mask
Figure FDA0003035099480000011
Second mask
Figure FDA0003035099480000012
Where (i, j) is the pixel position of the blood-soaked image in the RGB color space.
5. The method for constructing a gauze blood soaking amount estimation model based on feature engineering as claimed in claim 4, wherein: in step S3, two blood-soaked areas of the blood-soaked gauze image are obtained using a mask, which are defined as:
first blood-soaked area
Figure FDA0003035099480000013
Second blood-soaking area
Figure FDA0003035099480000014
Wherein (i, j) is the pixel position of the blood-soaked image in RGB color space, Bi,jAnd (3) obtaining HSV pixel vector values of the positions of the original blood-soaked gauze pictures (i, j).
6. The method for constructing a gauze blood soaking amount estimation model based on feature engineering as claimed in claim 5, wherein: in step S4, the hemoglobin amount is obtained by multiplying the ratio of the hemoglobin concentration to the blood-immersed region area, and the specific steps include:
(1) calculating two blood-soaked area masksThe number of the lower pixels is represented as PR1numAnd PR2num；
(2) The ratio of the blood-soaked area under the two masks is calculated respectively, namely the ratio of the whole image is calculated:
Figure FDA0003035099480000021
Figure FDA0003035099480000022
(3) normalization treatment of the patient hemoglobin concentration:
Figure FDA0003035099480000023
wherein Hbc represents the hemoglobin concentration of a current single patient, and Max and Min represent the maximum value and the minimum value of the hemoglobin concentrations of all patients respectively;
(4) the hemoglobin amount of the blood-soaked area under the two masks is respectively calculated and is defined as the product of the area ratio of the blood-soaked area and the normalized hemoglobin concentration:
Hgb1＝Hbc×AR1，
Hgb2＝Hbc×AR2。
7. the method for constructing a gauze blood soaking amount estimation model based on feature engineering as claimed in claim 6, wherein: in the step S4, for each blood-soaked gauze image, calculating a mean value and a variance of each channel of the blood-soaked area in the HSV color space, and calculating mean value and variance characteristics for the two generated blood-soaked areas respectively due to the existence of two blood-soaked area masks; these features are noted as: h1_ mean, H1_ std, S1_ mean, S1_ std, V1_ mean, V1_ std, H2_ mean, H2_ std, S2_ mean, S2_ std, V2_ mean, V2_ std, 12 features in total.
8. The method for constructing a gauze blood-soaking amount estimation model based on feature engineering as claimed in claim 7, wherein: in the step S4, extracting 14 features including the features Hgb1 and Hgb2 and the 12 features from each blood-soaked gauze image; extracting features from all pictures in the data set constructed in the step S1 to form an image feature set; and serially constructing the image feature set or parallelly constructing the image feature set.
9. The method for constructing a gauze blood-soaking amount estimation model based on feature engineering as claimed in claim 8, wherein: in the step S5, the gauze blood volume estimation machine learning model is a multiple linear regression model constructed on the constructed image feature set.
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