# Detection of Pediatric Femur Configuration on X-ray Images
## 4 Discussion  
In this work, we specified the feature set that unambiguously determines femur configuration, the defined corresponding image keypoints, and we constructed femur coordinate system derived from those features. Subsequently, we proposed the fully automatic keypoint detector. The performance of the algorithm was evaluated on new data and achieved satisfactory results. The proposed set of features reflected the strict examination protocol and is only valid for two-dimensional image data. Admittedly, modern acquisition systems enable more informative image data (e.g., MRI). Then, image processing is less demanding, and higher accuracy can be obtained for the detection and/or classification task. The main motivation of our work was to change the balance between data acquisition and image processing. Therefore, we used lower quality image data (still present in plenty of medical facilities) but simultaneously lowered the fatigue of specific and fragile group of subjects, considered in this study. This forced us to design a more sophisticated and complex image processing algorithm. Our image processing algorithm consisted of two estimators. One of them was based on CNN, and contrary to widely popular hand-engineering, we proposed to optimize network architecture automatically. The optimization algorithm accelerated largely the process of _hyperparameter_ tuning. What is worth noticing, in the optimization process, at least 10 network architectures resulted in similar loss function values. We can explicitly state that the given estimation problem can be solved via CNN. Both keypoint estimators work in parallel, and their result is used to evaluate the configuration of the femur. Each image frame is processed separately; therefore, no prior information is used to determine femur configuration. The important feature of this solution is that the error does not accumulate for images of one sequence, i.e., corresponding to one subject. The main benefit of both estimators is the end-to-end learning pattern. In general, this type of solution processes the input image data faster and with lower computational costs than, e.g., image patch based evaluation [21]. Admittedly, the accuracy of the method is lower than for projects where three-dimensional data are available alongside two-dimensional data [37; 38]. However, it is the input data quality responsible for this outcome, not the method itself. Additionally, if three-dimensional data are not available, the segmented bone image may not be directly connected to the actual bone configuration. For example, out of plane rotation will influence the shape greatly. Therefore, simple segmentation methods [37] cannot be applied in this study. The proposed algorithm of keypoint detection results in a decent accuracy, similar to [39; 40]. Given the troublesome characteristics of images, we believe it is a success. The whole algorithm of femur configuration detection resulted in a reliable outcome even for images of different distributions than training data. The train and development sets were mostly pediatric images. Two healthy adult subjects were introduced to increase the generality of the proposed solution. On the other hand, the test set was composed of merely adult subjects' images. In the future, it would be beneficial to validate the algorithm on a dataset composed of children's X-rays. An important aspect of this work is the lack of ground truth in medical image data. The reference values used in this study were influenced by human error. Obtaining reliable reference data for keypoint detection still remains an open problem. **Funding:** This research was partially supported by the statutory grant no. 0211/SBAD/0321. **Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Bioethics Committee of Poznan University of Medical Sciences (resolution 699/09). **Informed Consent Statement:** Informed consent was obtained from legal guardians of all subjects involved in the study.