Additional file 1 of CompositIA: an open-source automated quantification tool for body composition scores from thoraco-abdominal CT scans
Creators: name: Cabini, Raffaella Fiamma
name: Cozzi, Andrea
name: Leu, Svenja
name: Thelen, Benedikt
name: Krause, Rolf
name: Del Grande, Filippo
name: Pizzagalli, Diego Ulisse
name: Rizzo, Stefania Maria Rita
Publisher: name: figshare
Year: 2025
Subjects: subject: Space Science
subject: Medicine
subject: Cell Biology
subject: Physiology
subject: FOS: Biological sciences
subject: Pharmacology
subject: Environmental Sciences not elsewhere classified
subject: Sociology
subject: FOS: Sociology
subject: Information Systems not elsewhere classified

description: Additional file 1: Fig. S1. MultiResUNet architecture: the neural network consists of an encoder-decoder structure, each with 4 MutiRes blocks and connected by Residual Paths (Res Path). The encoder (left) has convolutional layers (Conv2D), Rectified Linear Unit (ReLU) activation, and MaxPooling, while the decoder uses convolutional layers, ReLU activation, and Transposed Convolutions. The final layer applies a sigmoid activation for output predictions. In the lower-left corner, the composition of a MultiRes Block is depicted, utilizing consecutive 2D convolutions with 3 × 3 kernels to factorize convolutions of 5 × 5 and 7 × 7 kernels. In the lower-right corner, the composition of the Residual Path with 4 convolutional blocks (ResPath 4). Fig. S2. U-net scheme: the neural network consists of an encoder-decoder architecture, each with 4 convolutional blocks. The encoder (left) has convolutional layers, ReLU activation, and MaxPooling, while the decoder uses convolutional layers, ReLU activation, and UpSampling. A middle block connects the encoder and decoder, and the final layer applies softmax for binary segmentations. Fig. S3. Overview of the voted regression method employed for the identification of L1 and L3 planes from thoraco-abdominal CT scans. 2D Sagittal projections are computed from 3D volumes. Then, random patches (P) are extracted from each projection (panel a). Each patch is processed through a CNN for regression of the L1 and L3 coordinates (panel b). Values obtained from multiple patches are finally merged via a voting method (panel c). Fig. S4. The left panel depicts the input image of the model, which is a combination of sagittal and coronal projections along the y-axis. The right panel illustrates the model’s output, composed of the combination of the sagittal and coronal ground truth maps highlighting the L1 and L3 vertebra centers with increasing intensity peaks. Fig. S5. Sagittal projections of three representative subjects from the independent test set with L1 and L3 ground truth (GT) and predicted (pred) centers. The left panel shows the subject with the lowest model performance for L1 cortical segmentation (vDSC = 0.53 for the cortical region), the center panel depicts the oldest subject in the dataset, and the right panel highlights a subject with a hip prosthesis. Ground truth and predicted centers for L1 and L3 vertebrae are indicated by distinct colors. Fig. S6. Segmentations for three representative subjects from the independent test set. The left panel shows the subject with the lowest model performance for L1 cortical segmentation (vDSC = 0.53 for the cortical region), the center panel depicts the oldest subject in the dataset, and the right panel highlights a subject with a hip prosthesis. The top row displays the ground truth segmentations, while the bottom row shows the segmentations predicted by the models. Fig. S7. Detection performance of L1 and L3 vertebrae centers, measured as the absolute distance (mm) between ground truth and predicted centers, as a function of patient age on the independent test set (blue) and on the k-fold cross-validation (red). Spearman's correlation coefficient (ρ) and associated p values are reported to assess the relationship between age and detection performance. Fig. S8. Segmentation performance of U-netL1 and U-netL3 models, measured by vDSC, as a function of patient age on the independent test set (blue) and on the k-fold cross-validation (red). Spearman's correlation coefficient (ρ) and associated p values are reported to assess the relationship between age and segmentation accuracy.