Accurate prostate volume estimation using multifeature active shape models on T2-weighted MRI.

By R. Toth, B. Bloch, Elisabeth Genega, N. Rofsky, R. Lenkinski, Mark A. Rosen, A. Kalyanpur, S. Pungavkar, Anant Madabhushi. 2011

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Iterative Interaction Training for Segmentation Editing Networks (2018, neutral): “Segmentation is one of the main medical image analysis tasks that when automated substantially facilitates morphological measurements and increase eﬃ-ciency in treatment planning [11,17,18].”
Fully Automatic Segmentation of the Prostate using Active Appearance Models (2012, methodology): “Toth et al [11] use an extension to Active Shape Models in which the models are trained on multiple features for each voxel based on combinations of Gaussian feature responses, the features being selected during training for their discrimination between prostate border and background.”
Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching (2015, background): “Recently, most studies in T2-weighted MR prostate segmentation focus on two types of methods: multi-atlas-based [4]–[7] and deformable-model-based [8], [9] segmentation methods.”
Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge (2013, methodology): “Comparing these metric scores to scores reported in literature for inter-observer variability we can see that they are at approximately at the same level (Pasquier et al., 2007; Costa et al., 2007; Klein et al., 2008; Makni et al., 2009; Toth et al., 2011b; Chandra et al., 2012; Gao et al., 2012b).”