Medical image analysis techniques require an initial localization and segmentation of the corresponding anatomical structures. As part of the VISCERAL Anatomy segmentation benchmarks, a hierarchical multi{atlas multi{structure segmentation approach guided by anatomical correlations is proposed (AnatSeg-Gspac). The method de nes a global alignment of the images and re nes locally the anatomical regions of interest for the smaller structures. In this paper, the method is evaluated in the VISCERAL Anatomy3 benchmark in twenty anatomical structures in both contrast{ enhanced and non{enhanced computed tomography (CT) scans. AnatSeg-Gspac obtained the lowest average Hausdor distance in 19 out of the 40 possible structure scores in the test set CT scans.
benchmarks to test multiple segmentation approaches on the same available medical dataset for an objective evaluation of the algorithms [JdTGM+14]. The VISCERAL data set has been manually annotated by radiologists and includes real medical images obtained from clinical routine in hospitals. The benchmarks are set up in a cloud environment platform designed to host large amounts of medical data with equal computing instances for the participating research groups [LMMH13].
A hierarchic Anatomical structure Segmentation Guided by spatial correlations (AnatSegGspac)[JdTM13, JdTGM+14, JdTM14b] has been previously proposed and tested in the rst two VISCERAL Anatomy benchmarks. This approach requires no interaction from the user and generates a robust segmentation for multiple anatomical structures with short re{training phase for new scan parameters or additional structures [JdTM14a]. The evaluation and results of AnatSeg-Gspac in the VISCERAL Anatomy3 benchmark are presented in the following sections. 2 2.1
Dataset For the VISCERAL Anatomy3 benchmark 20 CT contrast{enhanced of the trunk (CTce) and 20 CT whole body unenhanced (CTwb) with their manual annotations (up to 20 anatomical structures), were provided to the participants for training. For the implementation of AnatSeg-Gspac in this benchmark a subset of volumes (7) with all or the majority of manual annotations were selected per modality as atlases. Further information on the VISCERAL data set can be found in [JdTGM+14]. The proposed method performs a hierarchic multi-atlas multi-structure segmentation de ning anatomical regions of interest in their spatial domain. The bigger and high contrast anatomical structures are used as reference for smaller structures with low contrast, which are consequently harder to segment. The registration pipeline has been optimized to reduce the amount of registrations needed for the smaller structures obtaining also a robust localization. In Figure 1, a sample segmentation output for one unused training volume including all the anatomical structures evaluated in the VISCERAL benchmarks is shown. Further information on the AnatSeg-Gspac method can be found in the previously referenced papers. 3
For the Anatomy3 benchmark the test set included 10 CTce volumes and 10 CTwb scans. Twenty di erent evaluation metrics are provided to the participants about their algorithm performance for each anatomical structure. The evaluation phase is performed in the Azure cloud by the organizers with no intervention from the participants. Table 2: Average Hausdor distance results in CT whole body (CTwb) test set of the VISCERAL Anatomy3 benchmark (Anatomy3 Leaderboard, http://www.visceral.eu/Leaderboard/, as of 1 April 2015). The method AnatSeg-Gspac generates robust segmentations for the big structures like the lungs (best benchmark scores highlighted). Moreover, it also shows overall better results particularly for small structures like thyroid, gallbladder and both adrenal glands.
The proposed method obtained the lowest average Hausdor distance of the Anatomy3 benchmark in 12=20 structures in CTce (Table 1) and 7=20 structures in CTwb (Table 2). The DICE coe cient scores are also presented for all the methods submitted in the benchmark (Table 3 and Table 4). The proposed method showed robustness in the segmentation of multiple structures from two di erent imaging modalities using a small training set. Both the distance and overlap scores in this and the previous Anatomy benchmarks show AnatSeg-Gspac outperforms other algorithms in some of the smaller anatomical structures (e.g. both adrenal glands, gallbladder). It can also obtain the best overlap for bigger and high contrasted structures like the lungs.
A limitation of the method is the computation cost, mainly for the B{spline non-rigid registrations. Although the number of registrations and the size of registered regions are reduced using anatomical correlations, the execution time is around 13 hours for a complete CT volume. A faster code implementation and better selection of the relevant atlases may reduce the number of needed registrations and thus the execution time of the method.
The method can be extended to the other imaging modalities and include more anatomical structures with short re{training phases. This is particularly important for its application with new or di erent scanners contained in large not annotated data sets. Further clinical image analyses, that may require the location of additional structures, might also bene t from this feature or include the output locations of the method as an initialization step. 5
This work was supported by the EU/FP7 through VISCERAL (318068). [CRK+13] [Doi05]
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