## 4 Discussion  
Bone marrow assessment during NHL diagnosis is of great importance due to its many therapeutic and prognostic implications. In this study, bone marrow from an [18]FFDG PET/CT image dataset of patients with NHL is segmented and metabolically quantified through radiomics features. To perform bone marrow segmentation, the U-Net network trained for skeleton segmentation in [22] is used and retrained in two steps. First, it is retrained with pseudo-real bone marrow masks and then retrained with six manually segmented bone marrow masks. The bone marrow masks obtained from this retrained model imply better results than those previously obtained in [12], with a Dice index of 0.964 and an IoU of 0.933 compared to values of 0.867 and 0.79 for the same metrics with the [12] algorithm. In addition, the results show the location of the main regions of bone marrow, failing mainly at the edges of the segmentation. As for other studies in which bone marrow segmentation is performed, examples have only been found for specific regions, such as the spinal column [28], distal tibia [29], or pelvis [30]. No studies of whole-body bone marrow segmentation have been found. Although many other architectures can be used for this segmentation, the U-Net model is selected for the possibility of transfer learning from the model in [22]. In this article, this architecture was selected since it has been proven to be efficient in biomedical image segmentation, such as the studies of [PERSON] et al. [31] and [PERSON] et al. [32], which \begin{table}
\begin{tabular}{c c c c|c c|c c|c c|c c} \hline \hline  & \multicolumn{4}{c|}{**PET**} & \multicolumn{4}{c|}{**MFC**} & \multicolumn{4}{c}{**pGT**} \\ \cline{3-13}  & & \multicolumn{2}{c|}{**F1\_m ***} & \multicolumn{2}{c|}{**F1\_w**} & \multicolumn{2}{c|}{**F1\_m ***} & \multicolumn{2}{c|}{**F1\_w**} & \multicolumn{2}{c|}{**F1\_m ***} & \multicolumn{2}{c|}{**F1\_w**} \\  & & **[PERSON]** & **BW** & **Md** & **BW** & **Md** & **BW** & **Md** & **BW** & **Md** & **BW** & **Md** & **BW** \\ \hline \multirow{4}{*}{**NHL**} & ORIG & LR & 0 & LR & 0 & NB & 3 & MLP2 & 2 & MLP1 & 1 & GB & 2 \\  & RED & LR & 2 & LR & **2** & & & & & MLP2 & 3 & GB & 1 \\  & OS & GB & 1 & GB & 1 & SIG & 3 & LR & 0 & LIN & 0 & LIN & 0 \\  & OS\_R & MLP1 & 2 & MLP1 & 2 & & & & & GB & 0 & GB & 0 \\ \hline \multirow{4}{*}{**FL**} & ORIG & RF, LR & 2,3 & RF, LR & 2,3 & MLP1 & 3 & MLP2 & 0 & DT & 0 & MLP2 & 1 \\  & RED & RF & 0 & RF & 0 & RF & **3** & DT & 3 & LR & 2 & RF & 1 \\  & OS & RF & 1 & DT & 3 & MLP1 & 2 & MLP1 & 2 & MLP1 & 3 & MLP1 & 3 \\  & OS\_R & RF & 2 & RF & 1 & RF & **3** & LR & 3 & RF & 2 & RF & 2 \\ \hline \multirow{4}{*}{**DLBCL**} & ORIG & GB & **3** & GB & **3** & LR & 1,2,3 & MLP1 & 1 & RF & 0 & RF & 0 \\  & RED & GB, MLP2 & 1,3 & MLP1 & 3 & & & & & LR, KNN & 3 & RBF & 3 \\  & OS & MLP1 & 2 & GB & 1 & RF & 0,2 & DT & 1 & DT & 2 & GB & 3 \\  & OS\_R & MLP1 & 3 & MLP1 & 3 & & & & & RF & 3 & RF & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Machine learning model (**Md**) and BW for the metrics represented in Tables 4 and 5. Grey cells represent the lack of those groups, i.e., no significant features after reduction. Green cells represent crossover between the model with the best F1\_m* and the best F1\_w. The BW marked in bold is the only possible one for those groups and labeling due to feature reduction. achieved outstanding bone segmentation. In addition, and compared to other architectures, this handles small datasets better than other complex architectures. The quantitative bone marrow assessment methodology is also based on [12; 13], taking CT bone marrow segmentation and extracting radiomics features from this region in the [18]FFDG PET to quantify the functional image to evaluate bone marrow status in NHL instead of in MM. However, the feature extraction is extended to 124 features, compared to the 32 original ones, and the process of feature reduction is more precise.