join ( json_dir, 'combined_normalized_locations.json' ) with open ( json_path, 'r' ) as f : path_to_norm_loc = json. add_argument ( 'source_dir', nargs = 1, metavar = '', help = 'Directory containing "combined_normalized_locations.json".' ) args = parser.
![localizer service volume localizer service volume](https://static.wixstatic.com/media/430377_832bf8e08cf744ea987003efd84c189b~mv2.png)
#Localizer service volume series#
Since we want all slices from the same CT series to be in the same partition, we will separate them by hashing the Series Instance UID, which will result in identical hash outputs across all images within a single CT dataset. We also save each image’s Series Instance UID attribute for use in partitioning the data into bins used in training.
![localizer service volume localizer service volume](https://i1.daumcdn.net/thumb/C264x200/?fname=https://blog.kakaocdn.net/dn/x1kie/btqC86IGACU/EOUbxX9FU8WWJPmho6SwWk/img.png)
We want to include the pixel data from each DICOM image, reshaped to fit the pre-trained model’s required input shape, and also its associated normalized location. npz file format containing data that will be used in training. Now that all the files have a normalized location, the last step in preprocessing the data involves converting the DICOM files into a. We decided to use 12 easily identifiable anatomical landmarks between the head and the femur that are spaced throughout the range. We created a DICOM image viewer and annotation tool that allowed the user to label a slice with an annotation with a single keystroke. Thus, our first task involves labeling our data. Our network will be trained with supervised learning, a process that uses labeled training data to generate a function that best estimates a relationship between input and expected output. Files were named based on their Instance Number attribute. We developed a file organizer to standardize the file structure containing the images, splitting them into nested folders based on attributes of the DICOM files: Patient ID, Study Instance UID, and Series Instance UID. With data obtained from different sources, folders and files have no consistent naming scheme. We collected numerous sets of CT images containing tens of thousands of individual slices as DICOM files from various open access databases: In this project, we adapt some of the pre-trained models available through Keras for use in CT slice localization.Įven with a pre-trained model, additional input data is needed to train it to handle a new task. To speed up the training process and improve our resulting model, we utilize a method called transfer learning, where a model developed for a specific task is used as the base of a model for a related task. Training a network from scratch requires vast amounts of data and time, even with capable hardware.