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