app.json: drop redundant top-level patch_size (now read from Prediction.yml)

total-3mm/app.json

@@ -1,7 +1,7 @@
 {
     "display_name": "Segmentation: TotalSegmentator 3mm",
     "short_description": "<b>Description:</b><br>Lightweight KonfAI adaptation of <a href='https://github.com/wasserth/TotalSegmentator'>TotalSegmentator</a> trained at <b>3 mm resolution</b>, reducing GPU/RAM requirements while segmenting <b>118 anatomical structures</b> in whole-body CT.<br><br><b>How to cite:</b><br><cite>J. Wasserthal et al., <i>TotalSegmentator: Robust Segmentation of 104 Anatomical Structures in CT Images</i>, Radiology: AI, 2023.</cite>",
-    "description": "<b>Description:</b><br>KonfAI-optimized version of the original nnU-Net-based TotalSegmentator 3 mm model.<br><br><b>Capabilities:</b><br>\u2022 Whole-body CT segmentation of <b>118 structures</b> (organs, bones, muscles, vessels)<br>\u2022 Reduced computational footprint for lower memory and faster throughput<br>\u2022 <b>3 mm isotropic</b> inference for easier deployment on large datasets<br><br><b>Training data:</b><br>Trained on <b>1204 clinically-derived CT scans</b> with strong diversity in contrast phases, scanner types and pathologies, with expert-reviewed manual annotations<br><br><b>How to cite:</b><br><cite>J. Wasserthal et al., <i>TotalSegmentator: Robust Segmentation of 104 Anatomical Structures in CT Images</i>, Radiology: AI, 2023.</cite>",
+    "description": "<b>Description:</b><br>KonfAI-optimized version of the original nnU-Net-based TotalSegmentator 3 mm model.<br><br><b>Capabilities:</b><br>• Whole-body CT segmentation of <b>118 structures</b> (organs, bones, muscles, vessels)<br>• Reduced computational footprint for lower memory and faster throughput<br>• <b>3 mm isotropic</b> inference for easier deployment on large datasets<br><br><b>Training data:</b><br>Trained on <b>1204 clinically-derived CT scans</b> with strong diversity in contrast phases, scanner types and pathologies, with expert-reviewed manual annotations<br><br><b>How to cite:</b><br><cite>J. Wasserthal et al., <i>TotalSegmentator: Robust Segmentation of 104 Anatomical Structures in CT Images</i>, Radiology: AI, 2023.</cite>",
     "tta": 0,
     "mc_dropout": 0,
     "models": [
@@ -507,11 +507,6 @@
             "color": "#61EA4B"
         }
     },
-    "patch_size": [
-        96,
-        128,
-        160
-    ],
     "vram_plan": {
         "8": {
             "patch_size": [

total/app.json

@@ -1,7 +1,7 @@
 {
     "display_name": "Segmentation: Total Segmentator",
     "short_description": "<b>Description:</b><br>KonfAI-accelerated adaptation of <a href='https://github.com/wasserth/TotalSegmentator'>TotalSegmentator</a>, delivering fast whole-body CT segmentation of <b>118 anatomical structures</b> (1.5 mm resolution) with reduced inference cost compared to the original nnU-Net implementation.<br><br><b>How to cite:</b><br><cite>J. Wasserthal et al., <i>TotalSegmentator: Robust Segmentation of 104 Anatomical Structures in CT Images</i>, Radiology: AI, 2023.</cite>",
-    "description": "<b>Description:</b><br>This model is an optimized adaptation of the original <a href='https://github.com/wasserth/TotalSegmentator'>TotalSegmentator</a> for the <b>KonfAI</b> deep learning framework.<br><br><b>Capabilities:</b><br>\u2022 Segmentation of <b>118 anatomical classes</b> covering organs, bones, muscles, and vessels<br>\u2022 Enhanced runtime and memory efficiency vs. the original nnU-Net implementation<br>\u2022 High-resolution input: <b>1.5 mm isotropic</b><br><br><b>Training data:</b><br>Trained on a diverse dataset of <b>1204 whole-body CT examinations</b> including different scanners, acquisition settings, contrast phases, and major pathologies (27 organs, 59 bones, 10 muscles, 8 vessels), with manual expert-reviewed annotations<br><br><b>How to cite:</b><br><cite>J. Wasserthal et al., <i>TotalSegmentator: Robust Segmentation of 104 Anatomical Structures in CT Images</i>, Radiology: AI, 2023.</cite>",
+    "description": "<b>Description:</b><br>This model is an optimized adaptation of the original <a href='https://github.com/wasserth/TotalSegmentator'>TotalSegmentator</a> for the <b>KonfAI</b> deep learning framework.<br><br><b>Capabilities:</b><br>• Segmentation of <b>118 anatomical classes</b> covering organs, bones, muscles, and vessels<br>• Enhanced runtime and memory efficiency vs. the original nnU-Net implementation<br>• High-resolution input: <b>1.5 mm isotropic</b><br><br><b>Training data:</b><br>Trained on a diverse dataset of <b>1204 whole-body CT examinations</b> including different scanners, acquisition settings, contrast phases, and major pathologies (27 organs, 59 bones, 10 muscles, 8 vessels), with manual expert-reviewed annotations<br><br><b>How to cite:</b><br><cite>J. Wasserthal et al., <i>TotalSegmentator: Robust Segmentation of 104 Anatomical Structures in CT Images</i>, Radiology: AI, 2023.</cite>",
     "tta": 0,
     "mc_dropout": 0,
     "models": [
@@ -511,11 +511,6 @@
             "color": "#61EA4B"
         }
     },
-    "patch_size": [
-        96,
-        128,
-        160
-    ],
     "vram_plan": {
         "8": {
             "patch_size": [

total_mr-3mm/app.json

@@ -1,7 +1,7 @@
 {
     "display_name": "Segmentation: TotalSegmentator MRI 3mm",
-    "short_description": "<b>Description:</b><br>Lightweight KonfAI adaptation of <a href='https://github.com/wasserth/TotalSegmentator'>TotalSegmentator MRI</a>, enabling fast multimodal segmentation of <b>50 key anatomical structures</b> from <b>MRI and CT</b> scans at <b>3 mm</b> resolution, greatly reducing memory usage and inference time compared to the original nnU-Net workflow.<br><br><b>How to cite:</b><br><cite>T. Akinci D\u2019Antonoli et al., <i>TotalSegmentator MRI: Robust Sequence-Independent Segmentation of Multiple Anatomic Structures in MRI</i>, Radiology, 2025.</cite>",
-    "description": "<b>Description:</b><br>This model integrates the reduced-resolution MRI-3mm configuration of TotalSegmentator into the <b>KonfAI</b> accelerated inference pipeline for efficient MRI/CT deployment.<br><br><b>Capabilities:</b><br>\u2022 Segmentation of <b>50 essential anatomical structures</b> (major organs, key bones, large vessels)<br>\u2022 <b>3 mm</b> isotropic input for high-throughput processing and lower GPU requirements<br>\u2022 Robust to acquisition variability including scanner type, contrast, and sequence parameters<br><br><b>Training data:</b><br>Trained on a diverse cohort of <b>1143 clinical scans</b> including <b>616 MRI</b> (multi-site, multi-scanner, multi-sequence) and <b>527 CT</b>, with expert-validated reference masks<br><br>><b>How to cite:</b><br><cite>T. Akinci D\u2019Antonoli et al., <i>TotalSegmentator MRI: Robust Sequence-Independent Segmentation of Multiple Anatomic Structures in MRI</i>, Radiology, 2025.</cite>",
+    "short_description": "<b>Description:</b><br>Lightweight KonfAI adaptation of <a href='https://github.com/wasserth/TotalSegmentator'>TotalSegmentator MRI</a>, enabling fast multimodal segmentation of <b>50 key anatomical structures</b> from <b>MRI and CT</b> scans at <b>3 mm</b> resolution, greatly reducing memory usage and inference time compared to the original nnU-Net workflow.<br><br><b>How to cite:</b><br><cite>T. Akinci D’Antonoli et al., <i>TotalSegmentator MRI: Robust Sequence-Independent Segmentation of Multiple Anatomic Structures in MRI</i>, Radiology, 2025.</cite>",
+    "description": "<b>Description:</b><br>This model integrates the reduced-resolution MRI-3mm configuration of TotalSegmentator into the <b>KonfAI</b> accelerated inference pipeline for efficient MRI/CT deployment.<br><br><b>Capabilities:</b><br>• Segmentation of <b>50 essential anatomical structures</b> (major organs, key bones, large vessels)<br>• <b>3 mm</b> isotropic input for high-throughput processing and lower GPU requirements<br>• Robust to acquisition variability including scanner type, contrast, and sequence parameters<br><br><b>Training data:</b><br>Trained on a diverse cohort of <b>1143 clinical scans</b> including <b>616 MRI</b> (multi-site, multi-scanner, multi-sequence) and <b>527 CT</b>, with expert-validated reference masks<br><br>><b>How to cite:</b><br><cite>T. Akinci D’Antonoli et al., <i>TotalSegmentator MRI: Robust Sequence-Independent Segmentation of Multiple Anatomic Structures in MRI</i>, Radiology, 2025.</cite>",
     "tta": 0,
     "mc_dropout": 0,
     "models": [
@@ -239,11 +239,6 @@
             "color": "#8DD3C7"
         }
     },
-    "patch_size": [
-        96,
-        96,
-        128
-    ],
     "vram_plan": {
         "8": {
             "patch_size": [

total_mr/app.json

@@ -1,7 +1,7 @@
 {
     "display_name": "Segmentation: TotalSegmentator MRI",
-    "short_description": "<b>Description:</b><br>KonfAI-accelerated adaptation of <a href='https://github.com/wasserth/TotalSegmentator'>TotalSegmentator MRI</a>, delivering fast multimodal segmentation of <b>80 major anatomical structures</b> from <b>MRI and CT</b> scans, with significantly reduced inference overhead vs. the original nnU-Net workflow.<br><br><b>How to cite:</b><br><cite>T. Akinci D\u2019Antonoli et al., <i>TotalSegmentator MRI: Robust Sequence-Independent Segmentation of Multiple Anatomic Structures in MRI</i>, Radiology, 2025.</cite>",
-    "description": "<b>Description:</b><br>This model integrates TotalSegmentator MRI into the <b>KonfAI</b> inference framework to accelerate deployment in MRI/CT multimodal workflows.<br><br><b>Capabilities:</b><br>\u2022 Automatic segmentation of <b>80 major anatomical structures</b> (organs, vessels, skeleton, digestive system)<br>\u2022 Robust to <b>sequence variations</b> across scanners, contrasts, acquisition planes, and sites<br>\u2022 High-resolution input: <b>1.5 mm isotropic</b><br><br><b>Training data:</b><br>Trained on a highly diverse clinical dataset of <b>1143 scans</b> including <b>616 MRI</b> (30 scanners, 4 sites, many contrast types) and <b>527 CT</b> scans, with expert-validated manual segmentations <br><br><b>How to cite:</b><br><cite>T. Akinci D\u2019Antonoli et al., <i>TotalSegmentator MRI: Robust Sequence-Independent Segmentation of Multiple Anatomic Structures in MRI</i>, Radiology, 2025.</cite>",
+    "short_description": "<b>Description:</b><br>KonfAI-accelerated adaptation of <a href='https://github.com/wasserth/TotalSegmentator'>TotalSegmentator MRI</a>, delivering fast multimodal segmentation of <b>80 major anatomical structures</b> from <b>MRI and CT</b> scans, with significantly reduced inference overhead vs. the original nnU-Net workflow.<br><br><b>How to cite:</b><br><cite>T. Akinci D’Antonoli et al., <i>TotalSegmentator MRI: Robust Sequence-Independent Segmentation of Multiple Anatomic Structures in MRI</i>, Radiology, 2025.</cite>",
+    "description": "<b>Description:</b><br>This model integrates TotalSegmentator MRI into the <b>KonfAI</b> inference framework to accelerate deployment in MRI/CT multimodal workflows.<br><br><b>Capabilities:</b><br>• Automatic segmentation of <b>80 major anatomical structures</b> (organs, vessels, skeleton, digestive system)<br>• Robust to <b>sequence variations</b> across scanners, contrasts, acquisition planes, and sites<br>• High-resolution input: <b>1.5 mm isotropic</b><br><br><b>Training data:</b><br>Trained on a highly diverse clinical dataset of <b>1143 scans</b> including <b>616 MRI</b> (30 scanners, 4 sites, many contrast types) and <b>527 CT</b> scans, with expert-validated manual segmentations <br><br><b>How to cite:</b><br><cite>T. Akinci D’Antonoli et al., <i>TotalSegmentator MRI: Robust Sequence-Independent Segmentation of Multiple Anatomic Structures in MRI</i>, Radiology, 2025.</cite>",
     "tta": 0,
     "mc_dropout": 0,
     "models": [
@@ -240,11 +240,6 @@
             "color": "#8DD3C7"
         }
     },
-    "patch_size": [
-        96,
-        128,
-        160
-    ],
     "vram_plan": {
         "8": {
             "patch_size": [