  In practical applications, effectively segmenting cracks in large-scale
computed tomography (CT) images holds significant importance for understanding
the structural integrity of materials. However, classical methods and Machine
Learning algorithms often incur high computational costs when dealing with the
substantial size of input images. Hence, a robust algorithm is needed to
pre-detect crack regions, enabling focused analysis and reducing computational
overhead. The proposed approach addresses this challenge by offering a
streamlined method for identifying crack regions in CT images with high
probability. By efficiently identifying areas of interest, our algorithm allows
for a more focused examination of potential anomalies within the material
structure. Through comprehensive testing on both semi-synthetic and real 3D CT
images, we validate the efficiency of our approach in enhancing crack
segmentation while reducing computational resource requirements.
