\section{Proposed Framework}
\label{sec:proposed-framework}
To solve the challenge of diagnostic quality assessment and to standardize the required steps, we propose a framework of several Neural Networks that is able to process radiographs of ankles and to output their diagnostic quality.
It consists of the following steps: \textit{Recognition of radiographic view}, \textit{extraction of the region of interest (ROI)}, and \textit{quality assessment}.

\begin{figure}[htbp]
	% Caption and label go in the first argument and the figure contents
	% go in the second argument
	\floatconts{fig:proposed-framework:framework}{\caption{Schematic flow of a radiograph through the framework.
	For each radiograph the framework decides first which radiographic view was used.
	Depending on that decision the radiograph is passed to the corresponding region of interest (ROI) segmentation network. After segmentation, the resulting ROI is fed into the final quality prediction network, which outputs the quality assessment.
	}}{\includegraphics[width=1.0\linewidth]{images/quality-pipeline.png}}
\end{figure}

The first step relies on the fact that radiographs can be ordered hierarchically by radiographic view. A prediction of quality strongly depends on the view since corresponding criteria for radiographic views may differ.
The second step prepares the input for quality assessment by removing unnecessary information.

Each individual step can be used independently. But only within the whole framework they provide the possibility to decide whether an ankle radiograph is of high or low diagnostic quality, thereby directly supporting the radiographers in their decision process. A complete overview of the framework can be seen in \figureref{fig:proposed-framework:framework}. In the following, we describe each step in more detail.

\subsection{Recognition of the Radiographic View}\label{subsec:proposed-framework:recognition-of-the-radiographic-view}
The first step of the proposed framework consists of recognizing the radiographic view of the radiograph. This classification task is mandatory since radiographs of various radiographic views differ in quality assessment characteristics, as shown in \figureref{fig:datasets:diagnostic-quality-dataset:relevant-structures}. By dividing the quality assessment task for an ankle radiograph into a view-specific task, we facilitate the learning process of our networks, since the radiographs now belong to the same domain.


\subsection{Extraction of the ROI}\label{subsec:proposed-framework:extraction-of-the-roi}
While the entire radiograph is relevant for diagnosis, only a fraction is needed for assessing the quality of the standard projection (red marks in \figureref{fig:datasets:diagnostic-quality-dataset:relevant-structures}).
Based on this fact, the next step in the framework is to segment this ROI which contains the most information relevant for the diagnostic quality. An example ROI is shown in \figureref{fig:datasets:diagnostic-quality-dataset:quality-example}. Since there are different quality characteristics in the radiographic views, we trained Neural Networks individually for each view.
Besides removing irrelevant information, the benefit of extracting ROIs is that the subsequent quality assessment can operate on a standardized size and resolution of the relevant image part.

\subsection{Quality Assessment}\label{subsec:proposed-framework:quality-assessment}
Getting standardized ROIs of a particular radiographic view is the basis for assessing the diagnostic quality with high accuracy.
We use two different Neural Networks, one for each of the two radiographic views. These are trained individually on the \textit{anterior posterior} (\textit{AP}) and \textit{lateral} (\textit{LAT}) ROI, respectively and output the quality on a continuous scale from 1 to 3 (see \sectionref{sec:datasets:diagnostic-quality-dataset}).
