title: Prostatome: a combined anatomical and disease based MRI atlas of the prostate.
abstract: OBJECTIVE
  In this work, the authors introduce a novel framework, the anatomically constrained registration (AnCoR) scheme and apply it to create a fused anatomic-disease atlas of the prostate which the authors refer to as the prostatome. The prostatome combines a MRI based anatomic and a histology based disease atlas. Statistical imaging atlases allow for the integration of information across multiple scales and imaging modalities into a single canonical representation, in turn enabling a fused anatomical-disease representation which may facilitate the characterization of disease appearance relative to anatomic structures. While statistical atlases have been extensively developed and studied for the brain, approaches that have attempted to combine pathology and imaging data for study of prostate pathology are not extant. This works seeks to address this gap.
  
  
  METHODS
  The AnCoR framework optimizes a scoring function composed of two surface (prostate and central gland) misalignment measures and one intensity-based similarity term. This ensures the correct mapping of anatomic regions into the atlas, even when regional MRI intensities are inconsistent or highly variable between subjects. The framework allows for creation of an anatomic imaging and a disease atlas, while enabling their fusion into the anatomic imaging-disease atlas. The atlas presented here was constructed using 83 subjects with biopsy confirmed cancer who had pre-operative MRI (collected at two institutions) followed by radical prostatectomy. The imaging atlas results from mapping thein vivo MRI into the canonical space, while the anatomic regions serve as domain constraints. Elastic co-registration MRI and corresponding ex vivo histology provides "ground truth" mapping of cancer extent on in vivo imaging for 23 subjects.
  
  
  RESULTS
  AnCoR was evaluated relative to alternative construction strategies that use either MRI intensities or the prostate surface alone for registration. The AnCoR framework yielded a central gland Dice similarity coefficient (DSC) of 90%, and prostate DSC of 88%, while the misalignment of the urethra and verumontanum was found to be 3.45 mm, and 4.73 mm, respectively, which were measured to be significantly smaller compared to the alternative strategies. As might have been anticipated from our limited cohort of biopsy confirmed cancers, the disease atlas showed that most of the tumor extent was limited to the peripheral zone. Moreover, central gland tumors were typically larger in size, possibly because they are only discernible at a much later stage.
  
  
  CONCLUSIONS
  The authors presented the AnCoR framework to explicitly model anatomic constraints for the construction of a fused anatomic imaging-disease atlas. The framework was applied to constructing a preliminary version of an anatomic-disease atlas of the prostate, the prostatome. The prostatome could facilitate the quantitative characterization of gland morphology and imaging features of prostate cancer. These techniques, may be applied on a large sample size data set to create a fully developed prostatome that could serve as a spatial prior for targeted biopsies by urologists. Additionally, the AnCoR framework could allow for incorporation of complementary imaging and molecular data, thereby enabling their careful correlation for population based radio-omics studies.
authors.affiliation: Case Western Reserve University, Cleveland, Ohio 44106.
authors.forename: Mirabela
authors.initials: M
authors.lastname: Rusu
authors.affiliation: Boston University School of Medicine, Boston, Massachusetts 02118.
authors.forename: B Nicolas
authors.initials: BN
authors.lastname: Bloch
authors.affiliation: Boston University School of Medicine, Boston, Massachusetts 02118.
authors.forename: Carl C
authors.initials: CC
authors.lastname: Jaffe
authors.affiliation: Beth Israel Deaconess Medical Center, Boston, Massachusetts 02215.
authors.forename: Elizabeth M
authors.initials: EM
authors.lastname: Genega
authors.affiliation: UT Southwestern Medical Center, Dallas, Texas 75235.
authors.forename: Robert E
authors.initials: RE
authors.lastname: Lenkinski
authors.affiliation: UT Southwestern Medical Center, Dallas, Texas 75235.
authors.forename: Neil M
authors.initials: NM
authors.lastname: Rofsky
authors.affiliation: Riverside Research Institute, New York, New York 10038.
authors.forename: Ernest
authors.initials: E
authors.lastname: Feleppa
authors.affiliation: Case Western Reserve University, Cleveland, Ohio 44106.
authors.forename: Anant
authors.initials: A
authors.lastname: Madabhushi
mesh_terms: D000465:Algorithms; D001271:Atlases as Topic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D011467:Prostate / Q000033:anatomy & histology* / Q000473:pathology*; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000473:pathology / Q000601:surgery