A computer-implemented method for segmentation of a volumetric image dataset, said method comprising: performing, by an image-processing device, an initial segmentation of an input volumetric image dataset to obtain a main vessel structure and a plurality of broken segments; computing, by said image-processing device, a weighted path between said main vessel structure and a broken segment of said plurality of broken segments based on at least one or more parameters associated with a first voxel of said broken segment of said plurality of broken segments and a second voxel of said main vessel structure; generating, by said image-processing device, a graph comprising a plurality of first nodes, a plurality of second nodes, and a plurality of edges, wherein a first node from said plurality of first nodes corresponds to said first voxel of said broken segment of said plurality of broken segments, and wherein a second node from said plurality of second nodes corresponds to said second voxel of said main vessel structure, wherein each of said plurality of edges correspond to said computed weighted path; determining, by said image-processing device, a valid weighted path between said main vessel structure and said broken segment of said plurality of broken segments based on said computed weighted path and one or more pre-specified conditions, wherein said determined valid weighted path is a shortest path between said first voxel of said main vessel structure and said second voxel of said broken segment; filtering, by said image-processing device, said input volumetric image dataset to obtain a smoothed volumetric image dataset; converting, by said image-processing device, said smoothed volumetric image dataset to a binary volumetric image dataset based on a pre-defined threshold value; determining, by said image-processing device, a vector difference between a first gradient vector that corresponds to said smoothed volumetric image dataset and a second gradient vector that corresponds to said binary volumetric image dataset; generating, by said image processing device, a gradient field based on said determined vector difference; and generating, by said image-processing device, an output volumetric image dataset by performing a final segmentation on said gradient field by utilizing said determined valid weighted path as a constraint and applying graph cuts by finding boundaries passed by a maximal gradient flow. computing, by said image-processing device, a weighted path between said main vessel structure and a broken segment of said plurality of broken segments based on at least one or more parameters associated with a first voxel of said broken segment of said plurality of broken segments and a second voxel of said main vessel structure; generating, by said image-processing device, a graph comprising a plurality of first nodes, a plurality of second nodes, and a plurality of edges, wherein a first node from said plurality of first nodes corresponds to said first voxel of said broken segment of said plurality of broken segments, and wherein a second node from said plurality of second nodes corresponds to said second voxel of said main vessel structure, wherein each of said plurality of edges correspond to said computed weighted path; determining, by said image-processing device, a valid weighted path between said main vessel structure and said broken segment of said plurality of broken segments based on said computed weighted path and one or more pre-specified conditions, wherein said determined valid weighted path is a shortest path between said first voxel of said main vessel structure and said second voxel of said broken segment; filtering, by said image-processing device, said input volumetric image dataset to obtain a smoothed volumetric image dataset; converting, by said image-processing device, said smoothed volumetric image dataset to a binary volumetric image dataset based on a pre-defined threshold value; determining, by said image-processing device, a vector difference between a first gradient vector that corresponds to said smoothed volumetric image dataset and a second gradient vector that corresponds to said binary volumetric image dataset; generating, by said image processing device, a gradient field based on said determined vector difference; and generating, by said image-processing device, an output volumetric image dataset by performing a final segmentation on said gradient field by utilizing said determined valid weighted path as a constraint and applying graph cuts by finding boundaries passed by a maximal gradient flow.