Upload README.md

README.md

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+CoronaryDominance dataset
+
+DATA DESCRIPTION
+The data we present here includes three parts - the main dataset, the real distribution dataset, and the domain shift dataset. 
+P.S. IMPORTANT!!! The angiograms of the real distribution dataset are from the CoronarySYNTAX dataset. 
+
+The data we present here includes three parts - the main dataset, the real distribution dataset, and the domain shift dataset. The main dataset with 
+1,025 studies (319 of whom have left dominance, and 706 of whom have right dominance) from the Philips Allura Clarity cardiovascular imaging system is 
+for model training and hyper-parameters fine-tuning. For the main dataset, each study falls into one of five categories: bad quality, artifact, small 
+diameter, occlusion, or normal. ”Normal” is the largest category. We enhanced the main data set with additional left dominant cases and occlusions in 
+order to address the problem of imbalance
+
+The real distribution dataset consists of 400 randomly selected studies (54 left dominant, and 346 right dominant) for the Philips Allura Clarity 
+cardiovascular imaging system. Due to this, unlike the main dataset, the real distribution dataset follows the same distribution as all the studies in 
+the clinic. The real distribution dataset does not have group subdivision, so it includes studies with poor quality, artifacts, and high uncertainty. 
+The domain shift dataset comprises 149 studies (52 left and 97 right dominant) from the Philips Azurion cardiovascular imaging system. This dataset 
+also does not have group subdivision. However, we have enhanced it with an additional left dominant studies
+
+The structure of the data is as follows: The root directory contains two subdirectories: ”Left Dominance” and ”Right Dominance”, which contain 
+angiographic studies of left and right dominant patients, respectively. Each study consists of several angio-graphic views - gray-scale videos saved in 
+Numpy’s compressed array format (.npz). The study directory also contains two subdirectories named ”RCA” (for the right coronary artery) and ”LCA” (for 
+the left coronary artery), respectively. 
+
+The study folder follows the structure ”Study0xxxx study id”, where ”0xxxx” is the sequence number of the study and ”study id” is the study ID. The 
+name of the .npz file containing the angiographic view video matches the series ID. The study ID and the series ID are unique within the dataset and 
+follow the same format has the corresponding ID in DICOM (.dcm) files. Our data are fully anonymized; therefore the IDs in the dataset do not match 
+those in the original .dcm source files. The sequence number is unique within a specific dataset. We have introduced this for the convenience of 
+referencing, such as ”main dataset study 580” instead of ”1.1.11.11111.11.903846392438863822081872923423339322”.
+We extracted the primary and secondary angles from the fields (0018,1510) and (0018,1511) in the original .dcm file. You can consult the DICOM Standard 
+Browser for more information on these angles. In most cases, the is collaterals tag is True when the is occlusion tag is also True.
+
+‘‘Below is  the data structure in .npz files. 
+
+{Name:”pixel_array”,
Type:”Float array, size of frames×512×512”,
Range: “0 - 255”,
Description: “angiographic 3D view”
}
+
+{Name:”seriesid”,
Type:”string”,
Range: “consist of numbers and ‘.’ ”,
Description: “unique ID of an angiographic view”
}
+
+{Name:”studyid”,
Type:”string”,
Range: “consist of numbers and ’.’ ”,
Description: “unique ID of an angiographic study”
}
+
+{Name:”series_number”,
Type:”integer”,
Range: “0 - 1025”,
Description: “ID of an angiographic view”
}
+
+{Name:”is_collaterals”,
Type:”Boolean”,
Range: “{True, False, None}”, Description: “Collaterals in LCA”
}
+
+{Name:”primary_positioner_angle”,
Type:”Float ”,
Range: “-90 - 90, None”,
Description: “positioner angle, degree”
}
+
+{Name:”secondary_positioner_angle”,
Type:”Float ”,
Range: “-90 - 90, None”,
Description: “positioner angle, degree”}
+
+{Name:”is_occlusion”,
Type:”Boolean”,
Range: “ {True, False}, None”,
Description: “Occlusion in RCA”
}
+
+{Name:”is_undefined”,
Type:”Boolean”,
Range: “ {True, False}, None”,
Description: “Studies with high uncertainty”
}
+
+{Name:”is_artefact”,
Type:”Boolean”,
Range: “ {True, False}, None”,
Description: “The presence of artifacts”
}
+
+{Name:”artery_type”,
Type:”String”,
Range: “{LCA, RCA}”,
Description: “Coronary artery type”
}
+
+BACKGROUND INFORMATION AND TECHNICAL DETAILS ON THE DATASET
+
+We release a new dataset containing invasive coronary angiograms for the coronary dominance classification task, an essential aspect in assessing the 
+severity of coronary artery disease. The dataset holds 1,574 studies, including X-ray multi-view videos from two different interventional angiography 
+systems, which allows one to test the effect of domain shift. Each study has the following tags: bad quality, artifact, high uncertainty, and occlusion.
+Those tags help to classify dominance classification more accurately and allow to utilize the dataset for uncertainty estimation and outlier 
+detection.
+
+Coronary dominance classification is an essential step in the SYNTAX (Synergy Between Percutaneous Coronary Intervention with Taxus and Cardiac 
+Surgery) score estimation, which has become an crucial tool for assessing the severity of coronary artery disease. This classification is also 
+necessary when determining the severity of coronary lesions using the Gensini score.
+Coronary dominance is determined by the coronary artery branch that supplies the posterior descending artery (PDA). There are three main categories: 
+left dominance, right dominance, and co-dominance [3]. Approximately 70-80% of the population has right coronary dominance, while approximately 5-10% 
+have left coronary dominance. In some regions, the proportion of individuals with left coronary dominance may be as high as 20%. Co-dominance occurs in 
+approximately 10-20% ofcases, where the PDA receives blood from the left and right coronary arteries. It is
important to note that co-dominance is not 
+in the SYNTAX scoring system. Therefore, when dealing with complex cases involving arteries supplying the PDA almost equally, clinicians have to choose 
+’right dominance’ when calculating SYNTAX scores.
+
+Interventional cardiologists use X-ray video, known as angiographic view, to estimate the SYNTAX score and classify coronary dominance. To get these 
+videos,interventional cardiologists inject a contrast agent into the left (LCA) and right (RCA) coronary arteries. By capturing heart pulses from 
+different angles, an angiography study provides valuable information about the cardiovascular system.
The following factors may complicate the 
+classification of coronary dominance: a total occlusion, a small RCA diameter, a poor-quality angiogram, and artifacts. A RCA with a small diameter 
+means there is significant disagreement among experts about how to classify a patient. Cases with a poor-quality angiogram, which can lead to incorrect 
+diagnoses, typically have low-contrast medium filling. Studies that involve artifacts include those with pacing electrodes or sternal wires.  Our 
+dataset includes all these categories and allows us to measure the impact of each factor on classification metrics.
+
+The domain shift is an important problem when working with medical data. A change in a medical device's parameters or settings could significantly 
+impact the neural network's prediction accuracy. To allow ML researchers to experience this phenomenon, we present angiograms from two different 
+cardiovascular imaging systems in our dataset - Phillips Allura Clarity and Philips Azurion.
+
+Coronary angiography is an essential tool for medical imaging that allows for real-time observation of the heart and blood vessels in a 3D space. Using 
+X-ray technology, this technique captures images on 2D planes and creates video images of the internal organs and blood vessels. These views are not 
+synchronized in time. The study consists of the LCA and RCA views, which typically contain between 20 and 70 frames, 512×512 each. The pixels values 
+are in a range from 0 to 255. The angiograms from Phillips Azurion have a frame around the image which we deleted the frame and resized the images to 
+512×512.
+
+Six interventional cardiologists with experience ranging from 1 to 25 years provided ground truth information on coronary dominance. A moderator 
+PhD-level cardiologist with more than 15 years of experience verified their labels. We considered his labels to be a gold standard. He also labeled 
+studies with additional tags, such as occlusions and artifacts. The disagreement rate between the experts was 2.8%. After excluding complex categories 
+such as poor quality, artefacts, and small RCA diameters, the disagreement rate on the main data set dropped to 1.5%
+Our dataset of angiographic studies for coronary dominance classification could be a valuable benchmark for 3D multi-view methods. Since the data come 
+from different cardiovascular imaging systems, one can test the effect of domain shift. The dataset is useful for estimating uncertainty and detecting 
+outliers. If one trains a neural network using data without artifacts and then tests the network on a subset with artifacts, one can estimate the 
+algorithm’s stability concerning outliers. Studies involving RCA with a small diameter are particularly interesting for uncertainty estimation since 
+there is significant disagreement among experts regarding dominance classification in these studies.
+
+We recommend using normal and occlusion studies from the main dataset for model training and the other studies for testing only. For normal studies, 
+one could use both the RCA and LCA views for coronary dominance classification. However, in our opinion, the RCA views are more suitable for this 
+purpose. For studies with occlusion, only LCA views provide essential information for the dominance classification.
+