feat: Improve OCR sample selection and detection of clinical identifiers

This commit is contained in:
Ross
2026-05-11 12:27:28 +01:00
parent 7ac8517062
commit 916ac3b69c
+60 -9
View File
@@ -60,7 +60,7 @@
</div>
</div>
<div class="form-text mt-2">
OCR now runs sequentially on one sample image per series (capped) to reduce browser memory use.
OCR runs sequentially on multiple sample images per series (capped) to improve burned-in text detection.
</div>
</details>
</div>
@@ -188,7 +188,8 @@
};
const SITE_PREFIXES = ["ref", "rk9", "ra9", "rh8", "rbz", "rba"];
const OCR_MAX_SERIES_SAMPLES = 40;
const OCR_SERIES_SAMPLE_COUNT = 3;
const OCR_MAX_SAMPLES = 120;
const OCR_CANVAS_MAX = 768;
const DUPLICATE_BATCH_SIZE = 500;
@@ -665,7 +666,49 @@
function detectOcrPhi(text) {
const value = (text || "").toLowerCase();
return value.includes("accession") || value.includes("patient") || /(ref|rk9|rh8|ra9|rbz|rba)\d+/i.test(value);
// Catch common clinical identifier markers and identifier-like tokens.
if (value.includes("accession") || value.includes("patient") || value.includes("nhs") || value.includes("hospital")) {
return true;
}
return /(ref|rk9|rh8|ra9|rbz|rba)\d+/i.test(value)
|| /\b\d{3}[\s-]?\d{3}[\s-]?\d{4}\b/.test(value)
|| /\b\d{10}\b/.test(value)
|| /\b\d{6,8}\b/.test(value)
|| /\b(?:dob|d\.o\.b|birth|born)\b/.test(value);
}
function buildOcrSamples(records) {
const includeRecords = records.filter((record) => record.include);
const seriesMap = {};
for (const record of includeRecords) {
if (!seriesMap[record.seriesUid]) {
seriesMap[record.seriesUid] = [];
}
seriesMap[record.seriesUid].push(record);
}
const samples = [];
for (const seriesRecords of Object.values(seriesMap)) {
if (!seriesRecords.length) continue;
const candidateIndexes = [
0,
Math.floor((seriesRecords.length - 1) / 2),
seriesRecords.length - 1,
];
const used = new Set();
for (const index of candidateIndexes) {
if (used.has(index)) continue;
used.add(index);
samples.push(seriesRecords[index]);
if (used.size >= OCR_SERIES_SAMPLE_COUNT) break;
}
}
return samples.slice(0, OCR_MAX_SAMPLES);
}
async function getDownscaledOcrBlob(file) {
@@ -715,16 +758,18 @@
return;
}
const sampleBySeries = {};
for (const record of window.uploadPreview.records) {
if (!sampleBySeries[record.seriesUid]) sampleBySeries[record.seriesUid] = record;
if (!window.Tesseract || typeof window.Tesseract.createWorker !== "function") {
window.uploadPreview.records.forEach(r => { r.ocrWarning = false; });
toastr.error("OCR engine is unavailable in this browser session.");
return;
}
const samples = Object.values(sampleBySeries).slice(0, OCR_MAX_SERIES_SAMPLES);
const samples = buildOcrSamples(window.uploadPreview.records);
if (!samples.length) return;
const worker = await ensureOcrWorker();
let index = 0;
const flaggedSeriesUids = new Set();
for (const record of samples) {
index += 1;
@@ -733,11 +778,17 @@
const blob = await getDownscaledOcrBlob(record.file);
const result = await worker.recognize(blob);
const text = result?.data?.text || "";
record.ocrWarning = detectOcrPhi(text);
if (detectOcrPhi(text)) {
flaggedSeriesUids.add(record.seriesUid);
}
} catch (err) {
record.ocrWarning = false;
// Ignore per-image OCR errors and continue with other samples.
}
}
window.uploadPreview.records.forEach((record) => {
record.ocrWarning = record.include && flaggedSeriesUids.has(record.seriesUid);
});
}
async function calculateBlake3Hash(file) {