## 2 Biomedical Image Segmentation
### Challenges and Limitations of Traditional Methods
#### 2.2.4 Manual Parameter Tuning  
A common challenge associated with traditional segmentation methods is the need for manual parameter tuning. Methods often involve setting thresholds or parameters that are specific to the characteristics of the image or the target structure. Manual tuning makes  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Description** & **Reference** \\ \hline \multirow{4}{*}{Thresholding methods} & Fundamental technique based on pixel intensity values, separating regions using a defined threshold. Widely used for clear intensity differences in various biomedical imaging modalities. & [14; 15; 16; 17; 18] \\  & Emphasizes spatial coherence by considering groups of pixels with similar characteristics. Involves an iterative process for evolving regions based on criteria like intensity homogeneity or texture. Useful for variations in intensity within boundaries. & [19; 20; 21; 22; 23] \\ \hline \multirow{4}{*}{Edge-based methods} & Focuses on identifying boundaries or edges using gradient information, emphasizing discontinuities in pixel intensities for accurate segmentation. Valuable in scenarios with distinct intensity gradients requiring precise delineation. & [24; 25; 26; 27; 28; 29; 30] \\  & Intensity gradients requiring precise delineation. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of traditional approaches to biomedical image segmentation.  
these methods less adaptable to different datasets and potentially hinders the automation of segmentation processes, requiring expertise and user intervention [34].  
Limited Capacity for Handling Multimodal and 3D Data  
Traditional methods may encounter difficulties in handling multimodal or three-dimensional (3D) biomedical images. Medical imaging technologies often produce datasets with multiple modalities or volumetric information. Traditional methods designed for 2D, single-modal data may not seamlessly extend to the challenges posed by multimodal or 3D datasets, limiting their applicability in advanced medical imaging scenarios [35].  
Difficulty in Incorporating Prior Knowledge  
The integration of prior knowledge, such as anatomical atlases or expert annotations, into traditional methods can be challenging. While advanced techniques often leverage prior knowledge for improved segmentation accuracy, traditional methods may lack the mechanisms to effectively incorporate such information. This limitation can hinder the adaptability of segmentation approaches in scenarios where contextual information is crucial [32].  
Segmentation of Overlapping Structures  
Traditional methods may struggle with accurately segmenting overlapping structures, a common scenario in medical imaging where multiple anatomical components coexist. Thresholding, region-based, and edge-based methods may lack the specificity to handle intricate overlaps, leading to challenges in distinguishing and delineating individual structures accurately [36].  
In conclusion, while traditional methods have played a crucial role in the history of biomedical image segmentation, their challenges and limitations highlight the need for more advanced and adaptive techniques, such as those based on deep learning, to address the evolving complexities of medical imaging datasets. These limitations underscore the ongoing quest for methodologies that can provide robust and accurate segmentation across diverse biomedical imaging scenarios.