Update model card, app.json (patch size + VRAM plan) and inference configs

README.md

@@ -0,0 +1,54 @@
+---
+license: apache-2.0
+library_name: konfai
+pipeline_tag: image-segmentation
+tags:
+- medical-imaging
+- segmentation
+- multimodal
+- ct
+- mri
+- cbct
+- konfai
+---
+
+# ImpactSeg — Multimodal Body Segmentation
+
+Multimodal (**CBCT / MR / CT**) anatomical **body segmentation** (11 structures), built with
+[**KonfAI**](https://github.com/vboussot/KonfAI). One model handles all three modalities.
+
+## 🧩 Model
+
+| Model | Input | Output | Labels | Ensemble |
+|:--|:--|:--|:--:|:--:|
+| `body` | Volume (CT / MR / CBCT) | Segmentation | 11 | 1 |
+
+2.5D residual-encoder UNet · patch `[1, 192, 192]` · resampled to 3 mm.
+
+## 🚀 Usage
+
+```bash
+pip install impact_seg_konfai
+impact-seg-konfai segment body -i input.nii.gz -o output/
+```
+
+- **Generic runner:** `konfai-apps infer VBoussot/ImpactSeg:body -i input.nii.gz -o output/`
+- **Interactive:** [**SlicerKonfAI**](https://github.com/vboussot/SlicerKonfAI) — the ⚙ *Advanced* dialog overrides patch size and batch size.
+
+## ⚡ Performance & VRAM
+
+Benchmarked on an **NVIDIA RTX PRO 5000 (24 GB)**. The batch size is **auto-selected from your free GPU VRAM**.
+
+| Free VRAM | Batch (auto) | Peak VRAM |
+|:--|:--|:--|
+| 8 GB  | 160 | ~7 GB |
+| 16 GB | 320 | ~14 GB |
+| 24 GB | 512 | ~22 GB |
+
+≈ **16 s / case** on the benchmark volume (scales with case size). Override with `--patch-size` / `--batch-size`.
+
+## 🔗 Links
+
+- 🧠 **KonfAI:** [github.com/vboussot/KonfAI](https://github.com/vboussot/KonfAI)
+- 📦 **PyPI:** [impact_seg_konfai](https://pypi.org/project/impact_seg_konfai/)
+- 📄 **Paper:** [arXiv:2510.21358](https://arxiv.org/abs/2510.21358)

body/app.json

@@ -4,48 +4,114 @@
     "description": "<b>Description:</b><br><b>IMPACTSeg</b> is a multimodal anatomical segmentation model packaged for inference with <b>KonfAI</b>. It is designed for <b>CBCT, MR, and CT</b> scans and produces a consistent set of <b>11 labels</b> across modalities.<br><br><b>Training cohort:</b><br><b>232 CBCT + 282 MR + 955 CT</b> cases.<br><br><b>Use case:</b><br>Automated multimodal segmentation for downstream analysis, quantitative workflows, and clinical research pipelines.",
     "tta": 0,
     "mc_dropout": 0,
-    "models": ["Body.pt"],
+    "models": [
+        "Body.pt"
+    ],
     "inputs": {
-      "Volume": {
-        "display_name": "Input Volume",
-        "volume_type": "VOLUME",
-        "required": true
-      }
+        "Volume": {
+            "display_name": "Input Volume",
+            "volume_type": "VOLUME",
+            "required": true
+        }
     },
     "outputs": {
-      "Segmentation": {
-        "display_name": "Segmentation",
-        "volume_type": "SEGMENTATION",
-        "required": true
-      }
+        "Segmentation": {
+            "display_name": "Segmentation",
+            "volume_type": "SEGMENTATION",
+            "required": true
+        }
     },
     "inputs_evaluations": {
-      "Image": {
-        "Evaluation.yml": {
-          "Segmentation": {
-            "display_name": "Output Segmentation",
-            "volume_type": "VOLUME",
-            "required": true
-          },
-          "GT_Segmentation": {
-            "display_name": "GT Segmentation",
-            "volume_type": "VOLUME",
-            "required": true
-          }
+        "Image": {
+            "Evaluation.yml": {
+                "Segmentation": {
+                    "display_name": "Output Segmentation",
+                    "volume_type": "VOLUME",
+                    "required": true
+                },
+                "GT_Segmentation": {
+                    "display_name": "GT Segmentation",
+                    "volume_type": "VOLUME",
+                    "required": true
+                }
+            }
         }
-      }
     },
     "terminology": {
-      "1": { "name": "subcutaneous_tissue", "color": "#F4A261" },
-      "2": { "name": "muscle", "color": "#E76F51" },
-      "3": { "name": "abdominal_cavity", "color": "#2A9D8F" },
-      "4": { "name": "thoracic_cavity", "color": "#264653" },
-      "5": { "name": "bones", "color": "#E9C46A" },
-      "6": { "name": "gland_structure", "color": "#8AB17D" },
-      "7": { "name": "pericardium", "color": "#C8553D" },
-      "8": { "name": "prosthetic_breast_implant", "color": "#B56576" },
-      "9": { "name": "mediastinum", "color": "#577590" },
-      "10": { "name": "spinal_cord", "color": "#6D597A" },
-      "11": { "name": "brain", "color": "#43AA8B" }
+        "1": {
+            "name": "subcutaneous_tissue",
+            "color": "#F4A261"
+        },
+        "2": {
+            "name": "muscle",
+            "color": "#E76F51"
+        },
+        "3": {
+            "name": "abdominal_cavity",
+            "color": "#2A9D8F"
+        },
+        "4": {
+            "name": "thoracic_cavity",
+            "color": "#264653"
+        },
+        "5": {
+            "name": "bones",
+            "color": "#E9C46A"
+        },
+        "6": {
+            "name": "gland_structure",
+            "color": "#8AB17D"
+        },
+        "7": {
+            "name": "pericardium",
+            "color": "#C8553D"
+        },
+        "8": {
+            "name": "prosthetic_breast_implant",
+            "color": "#B56576"
+        },
+        "9": {
+            "name": "mediastinum",
+            "color": "#577590"
+        },
+        "10": {
+            "name": "spinal_cord",
+            "color": "#6D597A"
+        },
+        "11": {
+            "name": "brain",
+            "color": "#43AA8B"
+        }
+    },
+    "patch_size": [
+        1,
+        192,
+        192
+    ],
+    "vram_plan": {
+        "8": {
+            "patch_size": [
+                1,
+                192,
+                192
+            ],
+            "batch_size": 160
+        },
+        "16": {
+            "patch_size": [
+                1,
+                192,
+                192
+            ],
+            "batch_size": 320
+        },
+        "24": {
+            "patch_size": [
+                1,
+                192,
+                192
+            ],
+            "batch_size": 512
+        }
     }
 }