Cross-modality white matter lesion segmentation by modality de-indentification

Preložnik, Domen (avtor), Špiclin, Žiga (avtor)

Multiple sclerosis (MS) diagnosis and prognosis relies heavily on the accurate detection and segmentation of white matter lesions (WML) in magnetic resonance imaging (MRI). Different MRI sequences, particularly Fluid-Attenuated Inversion Recovery (FLAIR) and Double Inversion Recovery (DIR), offer complementary information about lesions but are rarely simultaneously acquired in clinical imaging protocols. We introduce a novel self-supervised modality sequential unlearning (SSMSU) adaptation technique that employs modality de-identification to extract modality-invariant features from MRI images, improving WML segmentation regardless of the input modality. Building upon the public nnU-Net framework, we introduce auxiliary modality classifiers at each resolution level and utilize confusion loss to explicitly suppress the modality-specific features while training on alternating modality inputs. We evaluated the approach on in-house dataset of 28 MS patients with paired FLAIR and DIR, MSSEG 2016 dataset of 53 subjects with paired FLAIR and proton density (DP), and 22 FLAIR test cases of MSLesSeg 2024. All cases had expert-annotated WML segmentation as reference. Experiments involved within- and between-dataset validation, comparing performances of single- and multi-modality single-channel, and multi-modality multi-channel training strategies based on Dice Similarity Coefficient (DSC), Lesion-wise True Positive Rate (LTPR), and Lesion-wise False Discovery Rate (LFDR). On in-house and MSSEG 2016 the SSMSU achieved best DSC and LTPR among single-channel models, with LFDR levels comparable to best values, while it attained the same level of performance to multi-channel models that required paired FLAIR/DIR or FLAIR/DP modalities. It ranked 2nd among single-channel methods on MSLesSeg 2024. Effectively suppressing modality-related information resulted in a technique that is cross-modal and delivers a flexible and robust automated WML segmentation tool.

• Jezik: Angleški jezik
• Ključne besede: magnetic resonance imaging, white-matter lesions, multimodal images, image segmentation, deep learning, self-supervised learning, validation
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FE - Fakulteta za elektrotehniko
• Status publikacije: Objavljeno
• Različica publikacije: Recenzirani rokopis
• Leto izida: 2026
• Št. strani: Str. 120-127
• Številčenje: Vol. 199
• PID: 20.500.12556/RUL-180172
• UDK: 004.93
• ISSN pri članku: 0167-8655
• DOI: 10.1016/j.patrec.2025.11.020
• COBISS.SI-ID: 257288963
• Datum objave v RUL: 04.03.2026

Gradivo je del revije

• Naslov: Pattern recognition letters : an official publication of the International Association for Pattern Recognition
• Skrajšan naslov: Pattern recogn. lett.
• Založnik: North-Holland
• ISSN: 0167-8655
• COBISS.SI-ID: 26103296

Licence

• Licenca: CC BY-NC-ND 4.0, Creative Commons Priznanje avtorstva-Nekomercialno-Brez predelav 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by-nc-nd/4.0/deed.sl
• Opis: Najbolj omejujoča licenca Creative Commons. Uporabniki lahko prenesejo in delijo delo v nekomercialne namene in ga ne smejo uporabiti za nobene druge namene.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: magnetnoresonančno slikanje, lezije v beli možganovini, večmodalne slike, segmentacija slik, globoko učenje, samonadzorovano učenje, validacija

Projekti

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: P2-0232
• Naslov: Analiza biomedicinskih slik in signalov

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: J2-3059
• Naslov: Sprotno prilagajanje načrta protonske in radioterapije