Please select the most similar patent number from A, B, C and D. Which number is?
Provided is a method for measuring fetal growth parameters. The measurement method comprises: obtaining an ultrasound image of at least one part of a fetus (S100); determining, according to a convolutional neural measurement model, the distribution region of a part to be measured in the ultrasound image, the convolutional neural measurement model training and determining a plurality of labeled ultrasound images of different fetal parts by means of a convolutional neural network (S200); highlighting and fitting the contour of the distribution region of the part to be measured (S300); by means of measuring the fitted contour corresponding to the part to be measured, obtaining growth parameters of different parts to be measured (S400). Also provided are a fetal growth parameter measurement system, ultrasound device, and storage medium. The fetal growth parameter measurement method, measurement system, ultrasound device, and storage medium can automatically measure the growth parameters of different fetal parts using a trained convolutional neural measurement model, improving the accuracy of measurement and improving the working efficiency of a doctor.
A: A method and apparatus for determining an optimal fetal standard view. Said method comprises: acquiring a standard view of each fetal ultrasound image among a plurality of fetal ultrasound images (101); determining the view score of the standard view of each fetal ultrasound image (102); and according to all the view scores, determining, from all the standard views, a standard view corresponding to the highest view score, and taking same as an optimal standard view (103). Once the standard view of the fetal ultrasound image is acquired, there is no need to perform manual analysis to determine the optimal standard view of the fetal ultrasound image, the view score of the standard view of the fetal ultrasound image can be automatically determined, and the standard view having the highest view score is intelligently selected from all the view scores, achieving the automatic determination of the optimal standard view, and the determination accuracy and efficiency of the optimal standard view of fetal ultrasound images can be improved, thereby achieving accurate acquisition of growth and development conditions of the fetus.
B: A follicle tracking method and system. The method comprises: acquiring ultrasound images of an ovarian tissue of a subject under test, the ultrasonic images being obtained at at least three different examination times, wherein the ovarian tissue comprises a target follicle (201); respectively determining follicle regions, on the ultrasound images obtained at at least three different examination times, corresponding to the target follicle to obtain at least three follicle regions, wherein the at least three follicle regions are follicle regions corresponding to the same target follicle (202); respectively determining growth parameters of the target follicle according to the at least three follicle regions (203); obtaining a growth trend chart of the target follicle according to the at least three growth parameters of the target follicle (204); and displaying the growth trend chart (205). According to the method, tracking detection is performed on the same target follicle in the ultrasonic images obtained at different examination times, and the growth parameter trend chart of the same target follicle is obtained, such that an operator can precisely evaluate an optimal follicle taking time according to the growth trend chart of the target follicle, thereby effectively improving the precision and working efficiency of evaluating a follicle taking time.
C: A lesion part identification method, identification apparatus, computer apparatus and readable storage medium. The method comprises: acquiring a first magnetic resonance image and a second magnetic resonance image by performing magnetic resonance scanning on a preset part of human body by applying different magnetic resonance scanning sequences (101); performing image registration on the first magnetic resonance image and the second magnetic resonance image (102); detecting the registered first magnetic resonance image and second magnetic resonance image by using a trained regression convolutional neural network model to obtain a first preset part area in the registered first magnetic resonance image and a second preset part area in the registered second magnetic resonance image (103); predicting the probability that the first preset part area belongs to a lesion area by using a first convolutional neural subnetwork in a trained dual training convolutional neural network model so as to obtain a first lesion probability, and predicting the probability that the second preset part area belongs to a lesion area by using a second convolutional neural subnetwork in the trained dual training convolutional neural network model so as to obtain a second lesion probability (104); and determining whether the preset part is a lesion part according to the first lesion probability and the second lesion probability (105). The present method may quite accurately identify lesion parts.
D: A pelvic floor measurement method. After a current ultrasonic pelvic floor image is acquired, pelvic floor key part points can be automatically recognized on the current ultrasonic pelvic floor image, and a measurement reference line is automatically marked for a doctor to view. Compared with the prior art where the doctor manually recognizes the key part points of the pelvic floor and manually marks the measurement reference line, the measurement process is simplified, the measurement time is shortened, and therefore the measuring efficiency is improved. Moreover, the pelvic floor measurement method in the present application is based on a mature deep learning intelligent algorithm, and a measurement reference line can be accurately marked, so that the measurement accuracy is improved. Further disclosed are a pelvic floor measurement system and an ultrasonic device. The pelvic floor measurement system and the ultrasonic device have the same beneficial effects as the pelvic floor measurement method.
Answer: A