Table 2 Disease binary classification based on individual brain structures.

From: Comparative validation of AI and non-AI methods in MRI volumetry to diagnose Parkinsonian syndromes

Case

Midbrain

Pons

Midbrain/pons

V3

Caudate

Putamen

Pallidum

Normal vs. PSP

V-Net

\({0.73\pm 0.06}\)

\({0.69\pm 0.03}^*\)

\({0.65\pm 0.08}\)

\({0.83\pm 0.09}^*\)

\({0.54\pm 0.04}\)

\({0.74\pm 0.01}^*\)

\({0.78\pm 0.05}^*\)

UNETR

\({0.69\pm 0.06}\)

\({0.64\pm 0.05}^*\)

\(0.60\pm 0.07\)

\({0.84\pm 0.08}^*\)

\({0.57\pm 0.03}\)

\({0.69\pm 0.03}^*\)

\({0.76\pm 0.05}\)

FS

\(0.70\pm 0.06\)

\(\mathbf {0.89\pm 0.05}\)

\(0.65\pm 0.08\)

\(0.82\pm 0.02\)

\(0.56\pm 0.06\)

\(0.62\pm 0.09\)

\(0.72\pm 0.11\)

Normal vs. MSA-P

V-Net

\({0.63\pm 0.05}\)

\({0.60\pm 0.05}\)

\(\mathbf {0.73\pm 0.03}\)

\(\mathbf {0.73\pm 0.03}\)

\({0.61\pm 0.02}\)

\(\mathbf {0.73\pm 0.03}\)

\({0.67\pm 0.10}\)

UNETR

\({0.61\pm 0.05}\)

\({0.67\pm 0.06}\)

\({0.70\pm 0.05}\)

\({0.70\pm 0.03}\)

\({0.59\pm 0.03}\)

\(\mathbf {0.73\pm 0.03}\)

\(0.65\pm 0.09\)

FS

\(0.64\pm 0.04\)

\(0.65\pm 0.04\)

\(0.70\pm 0.05\)

\(\mathbf {0.73\pm 0.03}\)

\(0.60\pm 0.06\)

\(0.70\pm 0.03\)

\(0.66\pm 0.08\)

Normal vs. MSA-C

V-Net

\({0.76\pm 0.11}\)

\({0.90\pm 0.04}\)

\(\mathbf {0.91}\pm \mathbf {0.02}\)

\({0.56\pm 0.02}\)

\({0.61\pm 0.01}\)

\({0.65\pm 0.11}\)

\({0.66\pm 0.09}\)

UNETR

\({0.73\pm 0.08}\)

\({0.86\pm 0.06}\)

\({0.81\pm 0.15}\)

\({0.58\pm 0.01}^*\)

\(0.57\pm 0.04\)

\(0.62\pm 0.10\)

\({0.66\pm 0.07}\)

FS

\(0.76\pm 0.10\)

\(0.90\pm 0.04\)

\(\mathbf {0.91\pm 0.02}\)

\(0.56\pm 0.01\)

\(0.62\pm 0.10\)

\(0.65\pm 0.10\)

\(0.71\pm 0.09\)

Normal vs. PD

V-Net

\({0.55\pm 0.02}\)

\({0.53\pm 0.02}\)

\({0.57\pm 0.04}\)

\({0.61\pm 0.07}\)

\({0.57\pm 0.02}\)

\({0.55\pm 0.03}\)

\({0.56\pm 0.02}\)

UNETR

\({0.58\pm 0.03}\)

\(0.55\pm 0.03\)

\(0.53\pm 0.04\)

\(\mathbf {0.63\pm 0.06}\)

\(0.55\pm 0.04\)

\(0.54\pm 0.05\)

\(0.54\pm 0.03\)

FS

\(0.56\pm 0.02\)

\(0.52\pm 0.01\)

\(0.57\pm 0.04\)

\(0.61\pm 0.08\)

\(0.54\pm 0.02\)

\(0.57\pm 0.01\)

\(0.54\pm 0.02\)

PD vs. PSP

V-Net

\({0.71\pm 0.07}\)

\({0.67\pm 0.03}\)

\({0.58\pm 0.07}\)

\({0.74\pm 0.09}\)

\({0.54\pm 0.02}\)

\({0.67\pm 0.03}\)

\(\mathbf {0.75\pm 0.03}\)

UNETR

\(0.67\pm 0.09\)

\(0.63\pm 0.07\)

\(0.57\pm 0.08\)

\({0.72\pm 0.10}\)

\({0.52\pm 0.01}\)

\({0.63\pm 0.01}\)

\({0.72\pm 0.05}\)

FS

\(0.69\pm 0.06\)

\(0.65\pm 0.04\)

\(0.58\pm 0.07\)

\(0.74\pm 0.11\)

\(0.52\pm 0.01\)

\(0.62\pm 0.03\)

\(0.71\pm 0.08\)

PD vs. MSA-P

V-Net

\({0.59\pm 0.07}\)

\({0.67\pm 0.02}\)

\({0.58\pm 0.06}\)

\({0.65\pm 0.08}\)

\({0.58\pm 0.02}\)

\({0.67\pm 0.01}^*\)

\({0.65\pm 0.07}\)

UNETR

\({0.56\pm 0.02}\)

\({0.64\pm 0.02}\)

\({0.67\pm 0.04}^*\)

\(0.59\pm 0.05\)

\({0.61\pm 0.03}\)

\({0.66\pm 0.01}^*\)

\({0.63\pm 0.05}\)

FS

\(0.59\pm 0.03\)

\(0.66\pm 0.02\)

\(0.58\pm 0.06\)

\(0.65\pm 0.08\)

\(0.59\pm 0.04\)

\(\mathbf {0.69\pm 0.04}\)

\(0.66\pm 0.07\)

PD vs. MSA-C

V-Net

\({0.74\pm 0.06}\)

\({0.90\pm 0.03}\)

\(\mathbf {0.94\pm 0.02}\)

\({0.56\pm 0.08}\)

\({0.59\pm 0.06}\)

\({0.50\pm 0.07}\)

\({0.64\pm 0.06}\)

UNETR

\({0.69\pm 0.02}\)

\({0.82\pm 0.11}\)

\({0.81\pm 0.16}\)

\(0.56\pm 0.08\)

\({0.58\pm 0.01}\)

\(0.57\pm 0.05\)

\({0.62\pm 0.05}\)

FS

\(0.71\pm 0.06\)

\(0.90\pm 0.03\)

\(\mathbf {0.94\pm 0.02}\)

\(0.57\pm 0.08\)

\(0.59\pm 0.06\)

\(0.59\pm 0.08\)

\(0.69\pm 0.10\)

  1. Segmentation AUC of CNN-based V-Net, ViT-based UNETR, and FS. Mean ± standard deviation for threefold cross-validation and midbrain-to-pons ratio segmentation are listed.
  2. *\(p < 0.05\) indicates a significant difference in AUC between the DL models and FS.
  3. The best result for each volume segmentation method based on FS and DL in binary classification is shown in bold.