FP7Индивидуална стипендия2014–2016

MAMMA · Spatio-Temporal Modeling for Enhanced Automated Detection and Classification of Non-Mass Lesions in Breast MRI

7РП — „Хора“ (Действия „Мария Кюри“)

Период: 2014-04-01 → 2016-03-31
Финансиране от ЕС: 243 848 €
Участници: 1
Схема: MC-IIF

Линиите свързват координатора с партньорите.

Накратко на български

Интелигентен софтуер за анализ на ЯМР изображения търси специфични изменения в гърдата, които не приличат на типични тумори. Това помага на лекарите да откриват по-точно раковите процеси и да избегнат ненужни биопсии при доброкачествени образувания.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Spatio-Temporal Modeling for Enhanced Automated Detection and Classification of Non-Mass Lesions in Breast MRI

The emphasis of this project lies in the development and evaluation of an intelligent and robust computer-assisted system for detecting and diagnosing breast lesions that present a non-mass-like enhancement and thus lead to a substantial improvement of the quality of breast MRI postprocessing, reduce the number of missed or misinterpreted cases leading to false-negative diagnosis, and avoid unnecessary biopsies for benign lesions or observation for malignant lesions. Non-mass-enhancing lesions exhibit a heterogeneous appearance in breast MRI with high variations in kinetic characteristics and typical morphological parameters, and have a lower reported speciﬁcity and sensitivity than mass-enhancing lesions. Combinations of morphological and temporal BIRAD descriptors have proven to be insuﬃcient to aid in the automated diﬀerential diagnosis of these lesions. We developed novel spatio-temporal descriptors that lead to a substantial improvement in diagnostic accuracy and eﬃciency and validated them in three speciﬁc experiments. Radiologists can benefit from this system by reduced interobserver variation and improved interpretation of mammograms for the presence or absence of malignant non-mass-like enhancing lesions. Adding novel algorithms to existing techniques in breast CAD systems will create a flexible toolbox that can be applied with minimal modifications to identifying other type of lesions or monitor response to chemotherapy. The expected results of this interdisciplinary project will deﬁnitely ﬁnd tremendous clinical applica-tions and impact on the successful treatment disease in the European society, and strongly addresses the overarching goals of the ”2020 Vision for the European Research Area”. Speciﬁcally, improving treatment outcome of major diseases such as breast cancer is a research priority in the European Union.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

The emphasis of this project lies in the development and evaluation of an intelligent and robust computer-assisted system for detecting and diagnosing breast lesions that present a non-mass-like enhancement and thus lead to a substantial improvement of the quality of breast MRI postprocessing, reduce the number of missed or misinterpreted cases leading to false-negative diagnosis, and avoid unnecessary biopsies for benign lesions or observation for malignant lesions.Non-mass-enhancing lesions represent a diagnostic challenge in breast MRI because of the high variance in morphological and kinetic characteristics and have a lower reported specificity and sensitivity than mass-enhancing lesions. Existing image analysis techniques have proven to be insufficient to capture the unique spatio-temporal behavior of these lesions and aid in the automated differential diagnosis of these lesions. We propose to develop, test and evaluate novel techniques for the detection and diagnosis of non-mass-like enhancing lesions and validate them in three specific experiments that will lead to a substantial improvement in diagnostic accuracy and efficiency.The mobility proposed in this project is for Prof. Anke Meyer-Baese, an expert in the field of pattern recognition techniques in medical imaging, to expand her skill set and research portfolio while working on a novel computer-aided diagnosis system for challenging breast lesions at the University of Maastricht, Department of Radiology in Netherlands. Prof. Meyer-Baese is a Full Professor at Florida State University, USA. A number of specific knowledge transferobjectives are outlined in this proposal. This project will have a strong impact on the European Research Area (ERA) through its innovative research goals, focused knowledge transfer and a new international collaboration, the training of medical and engineering students, and outreach measures.

Оригинален текст от CORDIS (на английски).

Участници

UNIVERSITEIT MAASTRICHT · MaastrichtКоординаторНидерландия