Confirma

Kate Madden Yee

Nov 9, 2004

Computer-aided detection (CAD) developer Confirma plans to highlight its breast MR image processing CAD technology, CADstream. Updated to version 3.1 in July, the device features multiplanar reformatting, angiogenesis maps, maximum intensity projections, and a CAD analysis tool, SureLoc, for MR-guided interventional procedures. SureLoc allows physicians to calculate coordinates or targets for MR-guided interventions using a breast coil at the point-of-procedure, according to the company.

Confirma

CADstream also has remote-viewing abilities, allowing MRI studies to be read anywhere within a high-speed network, and a 2D AutoROI feature, which identifies uptake and washout characteristics within a user-defined 2D region of interest or image slice.

Kirkland, WA-based Confirma forged several partnerships this year, including one with GE Healthcare to incorporate CADstream into GE's MRI systems for breast imaging. Another deal was struck with iCAD of Nashua, NH, to provide breast-cancer detection products with both mammography and MRI technology. Also this year, the company installed its 100th CADstream unit in the U.S.

By Kate Madden Yee
AuntMinnie.com staff writer
November 10, 2004

Copyright © 2004 AuntMinnie.com

(A) Free-response receiver operating characteristic (FROC) curve for DBT-DINO and DINOv2 (Meta AI) on the detection task. DINOv2 achieved higher sensitivity than DBT-DINO across most false-positive thresholds. (B) Detection rates of DBT-DINO and DINOv2 for lesions that were ultimately found to be benign (n = 70) and lesions that were found to be cancerous (n = 66). Error bars represent 95% CIs estimated via patient-level bootstrapping with 1000 repetitions. Both models showed similar detection rates for cancerous lesions, whereas DINOv2 detected more benign lesions than DBT-DINO. (C) Example bounding box predictions for DBT-DINO and DINOv2 on the detection test set. Each image is a single two-dimensional section from a three-dimensional digital breast tomosynthesis volume; the first column shows a mediolateral oblique view, and the second and third columns show craniocaudal views. Both models struggled to detect very small lesions, such as the 3-mm-diameter lesion shown in the first column. FP = false positive.

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(A) Free-response receiver operating characteristic (FROC) curve for DBT-DINO and DINOv2 (Meta AI) on the detection task. DINOv2 achieved higher sensitivity than DBT-DINO across most false-positive thresholds. (B) Detection rates of DBT-DINO and DINOv2 for lesions that were ultimately found to be benign (n = 70) and lesions that were found to be cancerous (n = 66). Error bars represent 95% CIs estimated via patient-level bootstrapping with 1000 repetitions. Both models showed similar detection rates for cancerous lesions, whereas DINOv2 detected more benign lesions than DBT-DINO. (C) Example bounding box predictions for DBT-DINO and DINOv2 on the detection test set. Each image is a single two-dimensional section from a three-dimensional digital breast tomosynthesis volume; the first column shows a mediolateral oblique view, and the second and third columns show craniocaudal views. Both models struggled to detect very small lesions, such as the 3-mm-diameter lesion shown in the first column. FP = false positive.

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