# Trackerless 3D Freehand Ultrasound Reconstruction: A Review
## 3 Results
### Quantitative Outcomes
#### 3.3.2 Generalization and Robustness of Methods  
Generalization capabilities were assessed by three studies. [PERSON] et al. [23] used US sweep datasets from forearms and carotids. Networks were trained independently on each dataset, combined, and with fine-tuning of the last two layers or the entire model specifically for the carotid dataset. The findings demonstrated that training exclusively on forearm data produced the least favorable results. Fine-tuning the last two layers for carotid data only enhanced performance comparably to retraining the network from scratch on solely carotid data. Moreover, extensive fine-tuning of the entire network further improved outcomes, suggesting that initial layers, which handle low-level features, are to some extent anatomy-specific. Training on both datasets involved the greatest amount of data and time but did not significantly surpass the results of comprehensive fine-tuning.  
[PERSON] et al. [22] evaluated reconstruction performance on the complete original test set with various variance-reduced training sets regarding the scanning protocol and anatomical variance. They showed, as depicted in Table 2 Methods E-F, that training only on straight sweeps increased the final drift and frame errors extensively. This performance reduction also applies, to a lesser extent, to train sets with a reduction in the number of subjects, and thus, anatomical variance. Meanwhile, training merely on more complex motions, like c- and s-shape, yielded much less substantial performance losses.  
[PERSON] et al. [18] specifically aimed to enhance model adaptability across different US domains, transitioning from a pre-trained network on a large transrectal dataset to a smaller transabdominal dataset. To tailor the model for specific domain characteristics, a paired-sampling strategy was implemented that processed samples from both domains simultaneously during training. This enabled the network to identify domain-invariant features while adapting to nuances specific to the target domain. The fine-tuning phase focused on aligning the learned features more closely with the characteristics of transabdominal scans. The effectiveness of this approach was demonstrated through the improved feature distribution alignment between the domains, indicating robust cross-domain adaptability. Notably, their Targeted Adaptive Ultrasound Volume Reconstruction (TAUVR)  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Addition** & **Dataset** & \begin{tabular}{c} **Transformation** \\ **Parameter** \\ \end{tabular} & **Improvement (\%)** & \begin{tabular}{c} **Improvement in Drift** \\ **Measures (\%)** \\ \end{tabular} \\ \hline \begin{tabular}{c} [PERSON] et al. [21] \\ A \(\rightarrow\) C \\ \end{tabular} & \begin{tabular}{c} Self-consistency strategy \\ 2 \\ \end{tabular} & \begin{tabular}{c} 1 \\ 2 \\ \end{tabular} & \begin{tabular}{c} \(\theta_{x},\theta_{y},\theta_{z}\) \\ \end{tabular} & MAE: 13.21 & FDR: 7.14 \\ \hline \begin{tabular}{c} [PERSON] et al. [20] \\ D \(\rightarrow\) E \\ \end{tabular} & \begin{tabular}{c} Frame, path, and sequence \\ level online learning \\ \end{tabular} & \begin{tabular}{c} 1 \\ 1 \\ \end{tabular} & \begin{tabular}{c} \(\theta_{x},\theta_{y},\theta_{z}\) \\ \end{tabular} & MAE: 3.85 & FDR: 4.75 \\ \hline \begin{tabular}{c} [PERSON] et al. [22] \\ C \(\rightarrow\) D \\ \end{tabular} & \
\begin{tabular}{c} M = 2 to M = 100 past and \\ future frames \\ \end{tabular} &
\begin{tabular}{c} 1 \\ 1 \\ \end{tabular} & N/A & Frame error: 64.15 & FD: 75.53 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative improvements due to the integration of strategies using contextual or temporal information derived from the ultrasound sequence, rather than 2 US frames.  
approach achieved comparable results to the network trained exclusively on the target domain, as shown in Table 2, methods A and B.