Unsupervised measurement of brain tumor volume on MR images.

We examined unsupervised methods of segmentation of MR images of the brain for measuring tumor volume in response to treatment. Two clustering methods were used: fuzzy c-means and a nonfuzzy clustering algorithm. Results were compared with volume segmentations by two supervised methods, k-nearest neighbors and region growing, and all results were compared with manual labelings. Results of individual segmentations are presented as well as comparisons on the application of the different methods with 10 data sets of patients with brain tumors. Unsupervised segmentation is preferred for measuring tumor volumes in response to treatment, as it eliminates operator dependency and may be adequate for delineation of the target volume in radiation therapy. Some obstacles need to be overcome, in particular regarding the detection of anatomically relevant tissue classes. This study shows that these improvements are possible.

Comparison of supervised MRI segmentation methods for tumor volume determination during therapy.

Two different multispectral pattern recognition methods are used to segment magnetic resonance images (MRI) of the brain for quantitative estimation of tumor volume and volume changes with therapy. A supervised k-nearest neighbor (kNN) rule and a semi-supervised fuzzy c-means (SFCM) method are used to segment MRI slice data. Tumor volumes as determined by the kNN and SFCM segmentation methods are compared with two reference methods, based on image grey scale, as a basis for an estimation of ground truth, namely: (a) a commonly used seed growing method that is applied to the contrast enhanced T1-weighted image, and (b) a manual segmentation method using a custom-designed graphical user interface applied to the same raw image (T1-weighted) dataset. Emphasis is placed on measurement of intra and inter observer reproducibility using the proposed methods. Intra- and interobserver variation for the kNN method was 9% and 5%, respectively. The results for the SFCM method was a little better at 6% and 4%, respectively. For the seed growing method, the intra-observer variation was 6% and the interobserver variation was 17%, significantly larger when compared with the multispectral methods. The absolute tumor volume determined by the multispectral segmentation methods was consistently smaller than that observed for the reference methods. The results of this study are found to be very patient case-dependent. The results for SFCM suggest that it should be useful for relative measurements of tumor volume during therapy, but further studies are required. This work demonstrates the need for minimally supervised or unsupervised methods for tumor volume measurements.

A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain.

Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms, and a supervised computational neural network. Initial clinical results are presented on normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. For a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed, with fuzz-c-means approaches being slightly preferred over feedforward cascade correlation results. Various facets of both approaches, such as supervised versus unsupervised learning, time complexity, and utility for the diagnostic process, are compared.

Transfection into mouse L cells of genes encoding two serologically and functionally distinct bovine class I MHC molecules from a MHC-homozygous animal: evidence for a second class I locus in cattle.

w10 and KN104 are distinct class I major histocompatibility complex (MHC) serological specificities present in Boran (Bos indicus) cattle. Although these specificities are commonly expressed together, they may also be expressed independently. To establish whether w10 and KN104, when expressed together, are on the same or different molecules, and whether a second class I MHC locus exists in cattle, genomic DNA from an animal homozygous for a haplotype encoding the w10 and KN104 specificities was transfected into thymidine kinase-deficient mouse L cells (Ltk- cells), and the transfected cells were screened with monoclonal antibodies (mAb) specific for the w10 or KN104 allospecificities. Two different populations of transfectants were identified: the cells of one population reacted only with w10-specific mAb, whereas those of the other population were recognized only by the KN104-specific mAb. Alloreactive cytotoxic T lymphocytes (CTL) also distinguished between the two populations. Two CTL clones, shown to be restricted by the KN104 specificity, killed only those L cells expressing molecules recognized by the KN104-reactive mAb. Of eight CTL clones which recognized class I molecules associated with the w10 specificity, four killed the L cells expressing the w10 specificity. The remaining four clones did not kill either population of transfectants. Finally, immunoprecipitation studies revealed that both populations express full-length bovine class I MHC molecules. These results demonstrate that the w10 and KN104 specificities are on distinct class I molecules. As the genes encoding these molecules were derived from a MHC-homozygous animal, the findings also provide strong evidence that there are at least two classical class I loci in cattle.