From 15cf06bb100dac04317aa5e4727646c81577f483 Mon Sep 17 00:00:00 2001 From: JISHNU P <cb.en.p2aid19017@cb.students.amrita.edu> Date: Fri, 3 Jul 2020 12:06:35 +0530 Subject: [PATCH] Update Read_me.txt --- Read_me.txt | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/Read_me.txt b/Read_me.txt index d04a659..a5b381d 100644 --- a/Read_me.txt +++ b/Read_me.txt @@ -8,4 +8,11 @@ https://hc18.grand-challenge.org/ The data is divided into a training set of 999 images and a test set of 335 images. The size of each 2D ultrasound image is 800 by 540 pixels with a pixel size ranging from 0.052 to 0.326 mm. The pixel size for each image can be found in the csv files: ‘training_set_pixel_size_and_HC.csv’ and ‘test_set_pixel_size.csv’. The training set also includes an image with the manual annotation of the head circumference for each HC, which was made by a trained sonographer. The csv file 'training_set_pixel_size_and_HC.csv ' includes the head circumference measurement (in millimeters) for each annotated HC in the training set. All filenames start with a number. There are 999 images in the trainingset, but the filenames only go to 805. Some ultrasound images were made during the same echoscopic examination and have therefore a very similar appearance. These images have an additional number in the filename in between "_" and "HC" (for example 010_HC.png and 010_2HC.png). Download link : https://zenodo.org/record/1327317#.Xv2xeSgzZPY -(176.5MB \ No newline at end of file +(176.5MB + +stage1 : Segment the fetal head part from the input images using "Marchov Random Fields for Segmentation.ipynb" + +stage 2 : Perform feature extraction on the segmented images using VGG16 model " VGG16 feature extraction from segmented images.ipynb" + +stage 3 : Use deep learning model to predict the head circumference for the input image from its features using " Predicting Head Circumference.ipynb " + " Distribution of Head Circumference.ipynb " \ No newline at end of file -- GitLab