# Learning Models for Bone Marrow Edema Detection in Magnetic Resonance Imaging
## 1 Introduction  
Bone marrow (BM) is a highly cellular connective tissue contained within the bones. Bone marrow edema (BME) or the more recently termed edema-like marrow signal intensity (ELMS) [1], is the generic term applied to bone marrow signal intensity changes in magnetic resonance imaging (MRI) and is characterized by areas of high signal intensity in fluid-sensitive sequences (T2 or proton-density (PD) weighted images with fat-suppression, or short tau inversion-recovery (STIR)), and areas of intermediate-to-low signal intensity in T1-weighted images [2]. MRI has been, in recent decades, the most common imaging modality for evaluating patients affected by pain in the bones or joints, for whom X-rays images are first assessed by clinicians as inconclusive [3]. Since BME is characterized by replacing fat-rich components with water-based components, MRI is the most adequate method for its detection; however, there are several challenges associated with this finding. Changes at the biochemical level in the bone marrow can occur physiologically during the development of the skeleton, which represents additional difficulty in diagnosing this clinical condition, especially in children and young adults. With such heterogeneity in imaging BME manifestations, visual detection is more susceptible to error and highly depends on the experience of the physician and the equipment used for image acquisition. Developing machine learning (ML) strategies for the classification of BME in MRI will help clinicians by reducing their workload and the time associated with decision-making, as well as decreasing the error related to the diagnostic challenges previously mentioned. However, the application of the pattern recognition power of deep learning applied to this problem is still in its early days. One of the biggest problems with these approaches is data scarcity, which limits the development of robust solutions. The proposal of regions of interest to reduce the search space for BME-related patterns has been widely explored [4; 5], reducing the amount of redundant information that should be ignored by the learning model. However, since these approaches require more clinical annotation efforts, intensity-based masking (IBM) is a promising strategy to select the part of an image that can be relevant to a prediction when the physiological signal that wants to be measured is related to the intensity of the pixels [6]. On the other hand, the small size of the data collection is prone to produce biased solutions, incapable of generalizing in real-world scenarios; therefore, the use of pre-trained networks represents an option to overcome this limitation [7]. The pre-trained approaches have been extensively used in the medical domain and in very close challenges, such as for malignant lesion detection in the bone using MRI [8; 9]. Works dedicated to BME detection in MRI are very scarce and are focused mainly on adult patients. In the work of [PERSON] et al. [4], a convolutional neural network (CNN) was trained using data augmentation and transfer learning to detect BME of the sacroiliac joint [4]. The ROIs containing the sacral and iliac bones were extracted from the MRI slices, and random noise was added to them in order to increase the robustness of the model to possible noise formed during the acquisition of the MRI. The learning model achieved an accuracy of 83% for images containing the full and non-cropped MRI scan and 93% for the images containing only the ROI. Another work developed learning models for osteoarthritis detection, which presents morphological and degenerative changes in the hip joint, which could be detected in MRI sequences by identifying abnormalities in the bone and cartilage, as is the case with BME [5]. A pre-trained DenseNet-100 was used to classify the patients as healthy or unhealthy, achieving a sensitivity of 73% and specificity of 92%. A three-dimensional CNN was trained for a more generalist detection of lesions, trying to identify abnormalities on the knee using MRI [10].