A novel multi-atlas strategy with dense deformation field reconstruction for abdominal and thoracic multi-organ segmentation from computed tomography

By Oliveira, B.; Queir\'os, S.; Morais, P.; Torres, H.R.; Gomes-Fonseca, J.; Fonseca

Medical Image Analysis



Anatomical evaluation of multiple abdominal and thoracic organs is generally performed with computed tomography images. Owing to the large field-of-view of these images, automatic segmentation strategies are typically required, facilitating the clinical evaluation. Multi-atlas segmentation (MAS) strategies have been widely used with this process, requiring multiple alignments between the target image and the set of known datasets, and subsequently fusing the alignment results to obtain the final segmentation. Nonetheless, current MAS strategies apply a global alignment of a deformable object, per organ, subdividing the segmentation process into multiple ones and losing the spatial information among nearby organs. This paper presents a novel MAS approach. First, a coarse-to-fine method with multiple global alignments (one per organ) is used. To make the method spatially coherent, these individual organs' global transformations are then fused in one using a dense deformation field reconstruction strategy. Second, from the candidate segmentations obtained, the final segmentation is estimated through an organ-based label fusion approach. The proposed method is evaluated and compared against a conventional MAS strategy through the segmentation of twelve abdominal and thoracic organs from the VISCERAL Anatomy benchmark. Average Dice coefficients for the liver, spleen, lungs and kidneys are all higher than 90%, are around 85% for the aorta, trachea and sternum and 70% for the pancreas, urinary bladder and gallbladder. The novel MAS strategy, with dense deformation field reconstruction, shows competitive results against other state-of-the-art methods, proving its added value for the segmentation of abdominal and thoracic organs, mainly for highly variable organs.


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