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app.py

@@ -2,6 +2,17 @@ import gradio as gr
 import numpy as np
 from PIL import Image, ImageFilter, ImageEnhance, ImageOps
 from transformers import pipeline
+from segment_neuroimaging import (
+    segment_nph, segment_ventricles, compute_evans_index,
+    compute_callosal_angle, compute_temporal_horn_width,
+    compute_third_ventricle_width, score_pvh, assess_desh,
+    create_overlay, add_annotations, create_comparison,
+    preprocess_image, create_roi_mask, Modality, COLORS
+)
+import cv2
+import json
+import tempfile
+import os
 
 # ---- Load models (cached on first use) ----
 classifier = pipeline("image-classification", model="google/vit-base-patch16-224")
@@ -80,7 +91,7 @@ def detect_objects(image, threshold):
     return (image, annotations)
 
 
-# ---- Tab 4: Segmentation ----
+# ---- Tab 4: General Segmentation ----
 def segment_image(image):
     if image is None:
         raise gr.Error("Please upload an image first.")
@@ -95,6 +106,114 @@ def segment_image(image):
     return (image, annotations)
 
 
+# ---- Tab 5: NPH Neuroimaging Analysis ----
+def analyze_nph(image, modality, overlay_alpha):
+    if image is None:
+        raise gr.Error("Please upload a brain MRI or CT image first.")
+
+    # Save temp file for the segmentation pipeline
+    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as f:
+        Image.fromarray(image).save(f.name)
+        temp_path = f.name
+
+    try:
+        # Map display name to modality key
+        modality_map = {
+            "Axial FLAIR": "FLAIR",
+            "Axial T1": "T1",
+            "Axial T2": "T2",
+            "Coronal T2": "T2",
+            "Axial T2 FFE": "T2",
+            "Sagittal T1": "T1",
+            "CT Head": "CT_HEAD",
+        }
+        mod_key = modality_map.get(modality, "T1")
+        is_coronal = "Coronal" in modality
+
+        result = segment_nph(
+            temp_path,
+            modality=mod_key,
+            compute_biomarkers=True,
+        )
+
+        # Build biomarker report
+        meta = result.metadata
+        report_lines = ["## NPH Biomarker Report\n"]
+
+        # Evans' Index
+        ei = meta.get("evans_index")
+        if ei is not None:
+            status = "**ABNORMAL (>0.3)**" if ei > 0.3 else "Normal"
+            report_lines.append(f"**Evans' Index:** {ei:.3f} -- {status}")
+
+        # Temporal Horn Width
+        thw = meta.get("temporal_horn_width_px", 0)
+        if thw > 0:
+            report_lines.append(f"**Temporal Horn Width:** {thw} px")
+
+        # Third Ventricle Width
+        tvw = meta.get("third_ventricle_width_px", 0)
+        if tvw > 0:
+            report_lines.append(f"**Third Ventricle Width:** {tvw} px")
+
+        # PVH Grade (FLAIR only)
+        pvh = meta.get("pvh_grade")
+        if pvh is not None:
+            grade_desc = {0: "None", 1: "Pencil-thin rim", 2: "Smooth halo", 3: "Irregular, deep WM"}
+            report_lines.append(f"**PVH Grade:** {pvh}/3 -- {grade_desc.get(pvh, '')}")
+
+        # DESH Assessment
+        desh = meta.get("is_desh_positive")
+        if desh is not None:
+            desh_str = "**POSITIVE**" if desh else "Negative"
+            desh_score = meta.get("desh_total_score", "")
+            report_lines.append(f"**DESH Pattern:** {desh_str} (score: {desh_score}/6)")
+
+        # DESH details
+        desh_details = meta.get("desh_details")
+        if desh_details:
+            report_lines.append(f"\n### DESH Components")
+            report_lines.append(f"- Ventriculomegaly: {desh_details.get('ventriculomegaly_score', 'N/A')}/2")
+            report_lines.append(f"- Sylvian dilation: {desh_details.get('sylvian_dilation_score', 'N/A')}/2")
+            report_lines.append(f"- Convexity tightness: {desh_details.get('convexity_tightness_score', 'N/A')}/2")
+
+        # Callosal angle for coronal images
+        if is_coronal:
+            vent_mask = result.masks.get("ventricles")
+            if vent_mask is not None:
+                ca_data = compute_callosal_angle(vent_mask)
+                ca = ca_data.get("callosal_angle_deg")
+                if ca is not None:
+                    ca_status = "Suggestive of NPH" if ca < 90 else ("Indeterminate" if ca < 120 else "Normal/ex vacuo")
+                    report_lines.append(f"\n**Callosal Angle:** {ca:.1f} deg -- {ca_status}")
+
+        report_lines.append("\n---")
+        report_lines.append("*Structures segmented:* " + ", ".join(meta.get("structures_found", [])))
+        report_lines.append("\n*Note: Pixel-based measurements without DICOM spacing are approximate.*")
+
+        report = "\n".join(report_lines)
+
+        # Build overlay with custom alpha
+        display_masks = {k: v for k, v in result.masks.items() if k != "skull"}
+        img_rgb, gray, _ = preprocess_image(temp_path)
+        overlay = create_overlay(img_rgb, display_masks, alpha=overlay_alpha)
+
+        biomarkers_for_annotation = {
+            k: v for k, v in meta.items()
+            if k in ("evans_index", "callosal_angle_deg", "temporal_horn_width_px",
+                      "pvh_grade", "is_desh_positive")
+        }
+        annotated = add_annotations(overlay, display_masks, f"{modality} -- NPH Analysis", biomarkers_for_annotation)
+
+        # Side-by-side comparison
+        comparison = create_comparison(img_rgb, annotated, f"{modality} -- NPH Analysis")
+
+        return annotated, comparison, report
+
+    finally:
+        os.unlink(temp_path)
+
+
 # ---- Build the UI ----
 css = """
 .main-title { text-align: center; margin-bottom: 0.5em; }
@@ -104,10 +223,56 @@ css = """
 with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
     gr.Markdown("# Image Processing Studio", elem_classes="main-title")
     gr.Markdown(
-        "Upload an image and explore filters, classification, object detection, and segmentation -- all powered by state-of-the-art models.",
+        "Upload an image and explore filters, classification, object detection, segmentation, and **NPH neuroimaging analysis** -- all powered by state-of-the-art models.",
         elem_classes="subtitle"
     )
 
+    with gr.Tab("NPH Analysis"):
+        gr.Markdown(
+            "### Normal Pressure Hydrocephalus -- Neuroimaging Segmentation & Biomarkers\n"
+            "Upload a brain MRI or CT scan to compute Evans' index, DESH pattern, PVH scoring, "
+            "callosal angle (coronal), and more."
+        )
+        with gr.Row():
+            with gr.Column():
+                nph_input = gr.Image(label="Upload Brain Scan", type="numpy")
+                nph_modality = gr.Dropdown(
+                    choices=[
+                        "Axial FLAIR", "Axial T1", "Axial T2",
+                        "Coronal T2", "Axial T2 FFE", "Sagittal T1",
+                        "CT Head"
+                    ],
+                    value="Axial FLAIR",
+                    label="Modality / Sequence"
+                )
+                nph_alpha = gr.Slider(
+                    minimum=0.1, maximum=0.9, value=0.45, step=0.05,
+                    label="Overlay Opacity"
+                )
+                nph_btn = gr.Button("Analyze for NPH", variant="primary")
+            with gr.Column():
+                nph_overlay = gr.Image(label="Segmentation Overlay", type="numpy")
+                nph_comparison = gr.Image(label="Side-by-Side Comparison", type="numpy")
+
+        nph_report = gr.Markdown(label="Biomarker Report")
+
+        nph_btn.click(
+            fn=analyze_nph,
+            inputs=[nph_input, nph_modality, nph_alpha],
+            outputs=[nph_overlay, nph_comparison, nph_report]
+        )
+
+        gr.Markdown(
+            "#### NPH Reference Values\n"
+            "| Biomarker | Normal | Suggestive of NPH |\n"
+            "|---|---|---|\n"
+            "| Evans' Index | < 0.3 | > 0.3 |\n"
+            "| Callosal Angle | > 120 deg | < 90 deg |\n"
+            "| Temporal Horn | < 2 mm | > 5 mm |\n"
+            "| PVH (FLAIR) | Grade 0 | Grade 2-3 |\n"
+            "| DESH Pattern | Absent | Present |"
+        )
+
     with gr.Tab("Filters & Effects"):
         with gr.Row():
             with gr.Column():