{ "id": 3804, "date": "2022-10-21T13:03:55", "date_gmt": "2022-10-21T11:03:55", "guid": { "rendered": "https:\/\/staging.mediaire.ai\/?p=3804" }, "modified": "2025-09-03T09:10:30", "modified_gmt": "2025-09-03T07:10:30", "slug": "segmentation-of-ms-lesions-accuracy-of-mdbrain", "status": "publish", "type": "post", "link": "https:\/\/mediaire.ai\/de\/segmentation-of-ms-lesions-accuracy-of-mdbrain\/", "title": { "rendered": "Segmentation of MS Lesions Accuracy of mdbrain 4.5 versus a pool of human experts" }, "content": { "rendered": "
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Presented at<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Congress of the European Society of Radiology<\/span> (2022)<\/p>\n

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Authors<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Dalbis T., Grilo J., Hitziger S., Ling W. X., Opalka J., Lemke A.<\/p>\n

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Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Purpose<\/strong><\/p>\n

The diagnosis of multiple sclerosis (MS) requires the assessment of lesion load from brain MRIs. Traditionally, MS lesions are manually annotated by radiologists, a process that is inefficient and error prone. The AI-software mdbrain leverages deep-learning to automatically segment MS lesions. Here, we assess the accuracy of the lesion-segmentation algorithm to be released in mdbrain 4.5 compared to SPM-SLS (http:\/\/atc.udg.edu\/salem\/slsToolbox\/) and to the inter-rater performance of 4 experts.<\/p>\n

Materials & Methods<\/strong><\/p>\n

mdbrain uses a deep neural network to segment lesions from a FLAIR scan. The network was trained using 280 annotated FLAIRs. Performances were tested on a separate dataset of 30 FLAIRs annotated by 4 experts. To assess segmentation accuracy, we computed the lesion-wise F1 score between each algorithm (mdbrain and SPM-SLS) and rater, averaged across raters. The inter-rater F1 was computed by comparing the annotation of each rater against the remaining 3. F1 scores were also computed for different lesion classes separately.<\/p>\n

Results<\/strong><\/p>\n

mdbrain achieved an F1 score of 0.72, which was larger than SPM-SLS (F1=0.55) but slightly smaller than the inter-rater (F1=0.75). F1 scores of mdbrain were larger than the inter-rater for juxtacortical (mdbrain F1=0.75; inter-rater F1=0.72) and infratentorial lesions (mdbrain F1=0.58; inter-rater F1=0.55), but smaller for periventricular (mdbrain F1=0.74; inter-rater F1=0.77) and deep-white matter lesions (mdbrain F1=0.70; inter-rater F1=0.76). An average time of 2 minutes was required by mdbrain to process a single scan (GPU-equipped machine).<\/p>\n

Discussion<\/strong><\/p>\n

The AI-software mdbrain 4.5 achieved a lesion-segmentation accuracy comparable to a pool of human experts and considerably higher than SPM-SLS.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Performance of lesion segmentationof mdbrain v4.5 against SPM v12 for F1 and DICE score. Benchmark = interrater accuracy (dashed line).<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>", "protected": false }, "excerpt": { "rendered": "

The diagnosis of multiple sclerosis (MS) requires the assessment of lesion load from brain MRIs. Traditionally, MS lesions are manually annotated by radiologists, a process that is inefficient and error prone. The AI-software mdbrain leverages deep-learning to automatically segment MS lesions.<\/p>", "protected": false }, "author": 1, "featured_media": 0, "comment_status": "open", "ping_status": "open", "sticky": false, "template": "", "format": "standard", "meta": { "_acf_changed": false, "footnotes": "" }, "categories": [ 14 ], "tags": [], "class_list": [ "post-3804", "post", "type-post", "status-publish", "format-standard", "hentry", "category-publications" ], "acf": [], "_links": { "self": [ { "href": "https:\/\/mediaire.ai\/de\/wp-json\/wp\/v2\/posts\/3804", "targetHints": { "allow": [ "GET" ] } } ], "collection": [ { "href": "https:\/\/mediaire.ai\/de\/wp-json\/wp\/v2\/posts" } ], "about": [ { "href": "https:\/\/mediaire.ai\/de\/wp-json\/wp\/v2\/types\/post" } ], "author": [ { "embeddable": true, "href": "https:\/\/mediaire.ai\/de\/wp-json\/wp\/v2\/users\/1" } ], "replies": [ { "embeddable": true, "href": "https:\/\/mediaire.ai\/de\/wp-json\/wp\/v2\/comments?post=3804" } ], "version-history": [ { "count": 5, "href": "https:\/\/mediaire.ai\/de\/wp-json\/wp\/v2\/posts\/3804\/revisions" } ], "predecessor-version": [ { "id": 21637, "href": "https:\/\/mediaire.ai\/de\/wp-json\/wp\/v2\/posts\/3804\/revisions\/21637" } ], "wp:attachment": [ { "href": "https:\/\/mediaire.ai\/de\/wp-json\/wp\/v2\/media?parent=3804" } ], "wp:term": [ { "taxonomy": "category", "embeddable": true, "href": "https:\/\/mediaire.ai\/de\/wp-json\/wp\/v2\/categories?post=3804" }, { "taxonomy": "post_tag", "embeddable": true, "href": "https:\/\/mediaire.ai\/de\/wp-json\/wp\/v2\/tags?post=3804" } ], "curies": [ { "name": "wp", "href": "https:\/\/api.w.org\/{rel}", "templated": true } ] } }