This repository contains a pipeline built for the Kaggle SIIM-ACR Pneumothorax Detection competition.
The goal of the competition is to develop a model to classify (and if present, segment) pneumothorax from a set of chest radiographic images in DICOM format.
A pneumothorax is an abnormal collection of air in the pleural space between the lung and the chest wall: it can result in death if not treated.
Pneumothorax is usually diagnosed by a radiologist on a chest x-ray, and can sometimes be very difficult to confirm.
An accurate AI algorithm to detect pneumothorax would be useful in a lot of clinical scenarios. #AI could be used to triage chest radiographs for priority interpretation, or to provide a more confident diagnosis for non-radiologists.
Instructions to download the data are included in the competition information.
The data is comprised of images in DICOM format (radiographies) and annotations in the form of image IDs and run-length-encoded (RLE) masks.
Some of the images contain instances of pneumothorax (collapsed lung), which are indicated by encoded binary masks in the annotations. Some images have multiple annotations.
1.2.276.0.7230010.3.1.4.8323329.14508.1517875252.443873,387620 23 996 33 986 43 977 51 968 58 962 65 956 70 952 74 949 76 946 79
Images without pneumothorax have a mask value of -1.
1.2.276.0.7230010.3.1.4.8323329.1034.1517875166.8504,-1
Running `examplefile.py`` it will be possible to review the example of a file structure we are working on.
The output will be the following (using the same file I used):
(0008, 0005) Specific Character Set CS: 'ISO_IR 100' (0008, 0016) SOP Class UID UI: Secondary Capture Image Storage (0008, 0018) SOP Instance UID UI: 1.2.276.0.7230010.3.1.4.8323329.300.1517875162.258081 (0008, 0020) Study Date DA: '19010101' (0008, 0030) Study Time TM: '000000.00' (0008, 0050) Accession Number SH: '' (0008, 0060) Modality CS: 'CR' (0008, 0064) Conversion Type CS: 'WSD' (0008, 0090) Referring Physician's Name PN: '' (0008, 103e) Series Description LO: 'view: AP' (0010, 0010) Patient's Name PN: '88c14312-3265-4a3f-b7bb-41818107d607' (0010, 0020) Patient ID LO: '88c14312-3265-4a3f-b7bb-41818107d607' (0010, 0030) Patient's Birth Date DA: '' (0010, 0040) Patient's Sex CS: 'F' (0010, 1010) Patient's Age AS: '58' (0018, 0015) Body Part Examined CS: 'CHEST' (0018, 5101) View Position CS: 'AP' (0020, 000d) Study Instance UID UI: 1.2.276.0.7230010.3.1.2.8323329.300.1517875162.258080 (0020, 000e) Series Instance UID UI: 1.2.276.0.7230010.3.1.3.8323329.300.1517875162.258079 (0020, 0010) Study ID SH: '' (0020, 0011) Series Number IS: "1" (0020, 0013) Instance Number IS: "1" (0020, 0020) Patient Orientation CS: '' (0028, 0002) Samples per Pixel US: 1 (0028, 0004) Photometric Interpretation CS: 'MONOCHROME2' (0028, 0010) Rows US: 1024 (0028, 0011) Columns US: 1024 (0028, 0030) Pixel Spacing DS: ['0.139', '0.139'] (0028, 0100) Bits Allocated US: 8 (0028, 0101) Bits Stored US: 8 (0028, 0102) High Bit US: 7 (0028, 0103) Pixel Representation US: 0 (0028, 2110) Lossy Image Compression CS: '01' (0028, 2114) Lossy Image Compression Method CS: 'ISO_10918_1' (7fe0, 0010) Pixel Data OB: Array of 154050 bytes
`
To build your full dataframe, you should run createdataframe.py.
In the dataframe folder you will find two pickles with the train and test dataframes I have already prepared as output examples for the script.