Object Detection Improves Tumour Segmentation in MR Images of Rare Brain Tumours.

ABSTRACT: Tumour lesion segmentation is a key step to study and characterise cancer from MR neuroradiological images. Presently, numerous deep learning segmentation architectures have been shown to perform well on the specific tumour type they are trained on (e.g., glioblastoma in brain hemispheres). However, a high performing network heavily trained on a given tumour type may perform poorly on a rare tumour type for which no labelled cases allows training or transfer learning. Yet, because some visual similarities exist nevertheless between common and rare tumours, in the lesion and around it, one may split the problem into two steps: object detection and segmentation. For each step, trained networks on common lesions could be used on rare ones following a domain adaptation scheme without extra fin

SUBMITTER: Chegraoui H 

PROVIDER: S-EPMC8657375 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature